Pipeline Script¶

This script

Import Packages¶

In [2]:
import os
import glob
import numpy as np
import pandas as pd
import scipy as sp
from scipy.stats import pearsonr
from scipy.stats import linregress
import seaborn as sns
import matplotlib.pyplot as plt
import re

Define paths and variables¶

In [3]:
# Set paths
fcpath = "/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d"
outpath = "~/Desktop/ImageData/PMACS_remote/analysis/postprocessing/"
clinpath = "~/Desktop/ImageData/PMACS_remote/data/clinical"
cestpath = "/Users/pecsok/Desktop/ImageData/PMACS_remote/data/cest/output_measures/UNI/"

# Choose what to analyse
networks = ["SomMot"] 
CESTnetworks = ["avgCEST_SomMot", "ctCEST_SomMot"]
CNB_scores = ["tap_tot"]
CNB_valids = ["tap_valid"] 
diag_scores = ["hstatus"]
demo_scores = ["sex", "age", "race","ethnic","dateDiff"]

# Make dataframe based on metrics of interest
grp_df = pd.DataFrame(columns = ["BBLID"] + ["Session"] + demo_scores + networks + CESTnetworks + CNB_scores + diag_scores)
print(grp_df)

# Initialize empty lists and vars
bblids = []
sesids = []

# Import group dataframes and set indices
subjlist = pd.read_csv("~/Desktop/ImageData/PMACS_remote/data/subject_list_111623.csv", sep=',') 
cnbmat = pd.read_csv(clinpath + "/cnb.csv", sep=',') 
diagmat = pd.read_csv(clinpath + "/diagnosis.csv", sep=',')
demomat = pd.read_csv(clinpath + "/demographics.csv", sep=',')
# cestmat = pd.read_csv(clinpath + "/demographics.csv", sep='\t') add grp CEST map here
cnbmat.set_index('bblid', inplace = True)
diagmat.set_index('bblid', inplace = True)
demomat.set_index('bblid', inplace = True)

# Set up renaming dictionary for CEST df
schaefer_indices = pd.read_csv('~/Desktop/ImageData/PMACS_remote/github/glucest-rsfmri/Schaefer2018_100Parcels_17Networks_order_FSLMNI152_2mm.Centroid_RAS.csv', sep=',') # Load the CSV with the mapping of numbers to labels
schaefer_dict = dict(zip(schaefer_indices['ROI Label'], schaefer_indices['ROI Name']))

print("yes")

Empty DataFrame
Columns: [BBLID, Session, sex, age, race, ethnic, dateDiff, SomMot, avgCEST_SomMot, ctCEST_SomMot, tap_tot, hstatus]
Index: []
yes

Choose which modules to run¶

In [4]:
runfcon = True
runCNB = True
rundiag = True
rundemo = True
runcest = True
run_grpanalysis = True

Stage 1: Create Group Data Frame¶

Import data, loop through subjects, and establish file paths¶

FIX THIS ERROR: /var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_77945/3898733492.py:72: FutureWarning: Setting an item of incompatible dtype is deprecated and will raise in a future error of pandas. Value 'PSY' has dtype incompatible with float64, please explicitly cast to a compatible dtype first. grp_df.loc[grp_df['BBLID'].astype(str) == bblid, grp_df.columns == diag_score] = diagnosis

In [6]:
# Generates list of all file names
folder_names = [folder for folder in glob.glob(os.path.join(fcpath, "*")) if os.path.isdir(folder)]
#subje

# Loop through subjects
for subj_path in folder_names: # loop through all rows of the spreadsheet
    if "sub" in subj_path:
        # Extract bblid id:
        print(subj_path)
        bblid = subj_path.split('-')[1]
        print("Processing subject " + bblid)
        # Extract session id: 
        items = os.listdir(subj_path)
        ses_folder = [item for item in items if item.startswith("ses")]
        ses = ses_folder[0].split('-')[1]
        ses_path = os.path.join(fcpath, subj_path, ses_folder[0]) # full path to session
        
        
        # Add to running list of IDs grp analysis later:
        bblids.append(bblid)
        sesids.append(ses)
        # Start new row in grp_df for this subject:
        ids = [bblid, ses]  # Values for the first two columns
        grp_df.loc[len(grp_df)] = ids + [float('nan')] * (len(grp_df.columns) - len(ids))
        print(ses_path)
        
        # Run a subset of subjs or exclude specific subjs.
        if bblid != "20902" and bblid != "93242"  and bblid != "20754" and bblid != "127065":
            ##################################################################################################
            ## FC
            ##################################################################################################
            if runfcon:
                os.path.join(fcpath, "sub-" + bblid, "ses-" + ses)
                ses_path = os.path.join(fcpath, subj_path, ses_folder[0]) # full path to session
                fcmat_glob = f"{ses_path}/func/*Schaefer117_measure-pearsoncorrelation_conmat.tsv"
                if os.path.isfile(glob.glob(fcmat_glob)[0]):
                    fcmat = pd.read_csv(glob.glob(fcmat_glob)[0], sep='\t') # read in fcmat
                    fcmat.set_index('Node', inplace = True)
                    # Loop through the networks
                    for network in networks:
                        print("Running " + network + " fcon")
                        # Select rows and columns corresponding to the network
                        network_fc = fcmat.loc[fcmat.index.str.contains(network), fcmat.columns[fcmat.columns.str.contains(network)]]
                        # Calculate avg network fc and add value to proper column in grp_df
                        print(network_fc.values.mean())
                        grp_df.loc[len(grp_df)-1, network] = network_fc.values.mean()
            
            ##################################################################################################
            ## CNB
            ##################################################################################################
            if runCNB:
                # Loop through the CNB scores
                for i in range(len(CNB_scores)):
                    CNB_score = CNB_scores[i]
                    CNB_valid = CNB_valids[i]
                    # Select score of interest & validity of that score
                    scores = cnbmat[CNB_score]
                    if int(bblid) in scores.index:
                        score = scores[int(bblid)]
                        valids = cnbmat[CNB_valid]
                        valid = str(valids[int(bblid)])
                        # If score was valid, add to grp_df
                        if 'V' in valid: 
                            grp_df.loc[grp_df['BBLID'] == bblid, grp_df.columns == CNB_score] = score 
            ##################################################################################################
            ## Diagnosis
            ##################################################################################################
            if rundiag:
                # Loop through the CNB scores
                for i in range(len(diag_scores)):
                    diag_score = diag_scores[i]
                    # Select score of interest and add to grp_df
                    diagnoses = diagmat[diag_score]
                    if int(bblid) in diagnoses.index:
                        diagnosis = diagnoses[int(bblid)]
                        grp_df.loc[grp_df['BBLID'].astype(str) == bblid, grp_df.columns == diag_score] = diagnosis 
                    else:
                        diagnosis = "Unknown"
                        grp_df.loc[grp_df['BBLID'].astype(str) == bblid, grp_df.columns == diag_score] = diagnosis 

            ##################################################################################################
            ## Demographics
            ##################################################################################################
            if rundemo:
                # Loop through the CNB scores
                for i in range(len(demo_scores)):
                    demo_score = demo_scores[i]
                    # Select metric of interest
                    scores = demomat[demo_score]
                    if int(bblid) in scores.index:
                        score = scores[int(bblid)]
                        # Add to grp_df
                        grp_df.loc[grp_df['BBLID'] == bblid, grp_df.columns == demo_score] = score 

            ##################################################################################################
            ## CEST
            ##################################################################################################
            if runcest and bblid != "88760" : #88760's CEST output is empty for some reason.
                print("Processing " + bblid + "'s CEST data'")
                # Extract Glu Session ID
                if bblid in subjlist['BBLID'].astype(str).values:
                    gluses = subjlist.loc[subjlist['BBLID'].astype(str) == bblid, 'SCANID_CEST'].values[0].astype(str) #.
                    cestid = bblid + "_" + gluses
                    print(cestid)
                    # Import data
                    for network in networks:
                        cest_pattern = cestpath + cestid + "/" + cestid + "-2d-GluCEST-s100_7-" + network + "-measures_UNI.tsv"
                        cestfile = glob.glob(cest_pattern)
                        for file in cestfile:
                            if os.path.isfile(file):
                                cestmat = pd.read_csv(file, sep='\t') 
                                means = [] 
                                counts = []
                                col_name = "avgCEST_" + network # for grp_df
                                ct_name = "ctCEST_" + network # for grp_df
                                for index, value in enumerate(cestmat.loc[0,:]):
                                     if "Mean" in cestmat.columns[index] and not np.isnan(value):
                                        # cestmat.at[0, cestmat.columns[index]] = float(value) * float(cestmat.iloc[0, index + 1])
                                        means.append(cestmat.at[0, cestmat.columns[index]])
                                        counts.append(cestmat.at[0, cestmat.columns[index + 1]])
                                if sum(counts) == 0:
                                    grp_df.loc[grp_df['BBLID'] == bblid, grp_df.columns == col_name] = "NaN"
                                    grp_df.loc[grp_df['BBLID'] == bblid, grp_df.columns == col_name] = "NaN"
                                else:
                                    grp_df.loc[grp_df['BBLID'] == bblid, grp_df.columns == col_name] = sum(means) # / sum(counts)
                                    grp_df.loc[grp_df['BBLID'] == bblid, grp_df.columns == ct_name] = sum(counts) # / sum(counts)
                             
                             
print(grp_df)
# sum_of_mean_columns now contains the sum of values in columns with "Mean" in the column name.

/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-19830
Processing subject 19830
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-19830/ses-10789
Running SomMot fcon
0.5043232243880017
Processing 19830's CEST data'
19830_10789
SomMot
[743]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20645
Processing subject 20645
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20645/ses-11260
Running SomMot fcon
0.5720617380987514
Processing 20645's CEST data'
20645_11260
SomMot
[639]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-125511
Processing subject 125511
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-125511/ses-10906
Running SomMot fcon
nan
Processing 125511's CEST data'
125511_10906
SomMot
[535]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-116019
Processing subject 116019
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-116019/ses-11135
Running SomMot fcon
0.476985742874784
Processing 116019's CEST data'
116019_11135
SomMot
[506]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-19790
Processing subject 19790
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-19790/ses-10819
Running SomMot fcon
0.5175497923745217
Processing 19790's CEST data'
19790_10819
SomMot
[598]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-80557
Processing subject 80557
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-80557/ses-10738
Running SomMot fcon
0.5305007860520911
Processing 80557's CEST data'
80557_10738
SomMot
[731]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20642
Processing subject 20642
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20642/ses-11261
Running SomMot fcon
0.47426628025063183
Processing 20642's CEST data'
20642_11261
SomMot
[640]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20180
Processing subject 20180
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20180/ses-11011
Running SomMot fcon
0.20778656869276968
Processing 20180's CEST data'
20180_11011
SomMot
[427]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-139272
Processing subject 139272
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-139272/ses-10739
Running SomMot fcon
0.27218497102444306
Processing 139272's CEST data'
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-121085
Processing subject 121085
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-121085/ses-10851
Running SomMot fcon
0.43996840510989793
Processing 121085's CEST data'
121085_10851
SomMot
[683]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-93292
Processing subject 93292
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-93292/ses-10938
Running SomMot fcon
0.45810106213875146
Processing 93292's CEST data'
93292_10938
SomMot
[563]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-90281
Processing subject 90281
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-90281/ses-10902
Running SomMot fcon
0.49504676313798956
Processing 90281's CEST data'
90281_10902
SomMot
[543]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-96659
Processing subject 96659
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-96659/ses-11096
Running SomMot fcon
0.3630628571620404
Processing 96659's CEST data'
96659_11096
SomMot
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-93274
Processing subject 93274
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-93274/ses-10765
Running SomMot fcon
0.43816497855689907
Processing 93274's CEST data'
93274_10765
SomMot
[707]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-106880
Processing subject 106880
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-106880/ses-10699
Running SomMot fcon
0.33084857768166126
Processing 106880's CEST data'
106880_10699
SomMot
[499]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-90077
Processing subject 90077
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-90077/ses-10962
Running SomMot fcon
0.2762654417352368
Processing 90077's CEST data'
90077_10962
SomMot
[718]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-121407
Processing subject 121407
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-121407/ses-10688
Running SomMot fcon
0.38987538916650333
Processing 121407's CEST data'
121407_10688
SomMot
[716]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-102041
Processing subject 102041
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-102041/ses-10821
Running SomMot fcon
0.5190933819359578
Processing 102041's CEST data'
102041_10675
SomMot
[574]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-119791
Processing subject 119791
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-119791/ses-10705
Running SomMot fcon
0.371118070292174
Processing 119791's CEST data'
119791_10705
SomMot
[613]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-106057
Processing subject 106057
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-106057/ses-11122
Running SomMot fcon
0.6162349104607715
Processing 106057's CEST data'
106057_11122
SomMot
[457]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20011
Processing subject 20011
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20011/ses-10888
Running SomMot fcon
0.5370066728124016
Processing 20011's CEST data'
20011_10888
SomMot
[576]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20543
Processing subject 20543
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-20543/ses-11259
Running SomMot fcon
0.5627042385252455
Processing 20543's CEST data'
20543_11259
SomMot
[723]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-87225
Processing subject 87225
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-87225/ses-10933
Running SomMot fcon
0.47038865250074874
Processing 87225's CEST data'
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-19981
Processing subject 19981
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-19981/ses-11106
Running SomMot fcon
0.6256415123250758
Processing 19981's CEST data'
19981_11106
SomMot
[589]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-132641
Processing subject 132641
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-132641/ses-10692
Running SomMot fcon
0.6318741942018791
Processing 132641's CEST data'
132641_10692
SomMot
[720]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-88608
Processing subject 88608
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-88608/ses-10764
Running SomMot fcon
0.32327147547042934
Processing 88608's CEST data'
88608_12108
SomMot
[680]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-97994
Processing subject 97994
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-97994/ses-11114
Running SomMot fcon
0.4575562573424146
Processing 97994's CEST data'
97994_11114
SomMot
[668]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-105979
Processing subject 105979
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-105979/ses-10791
Running SomMot fcon
0.37223398737529784
Processing 105979's CEST data'
105979_10791
SomMot
[640]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-92155
Processing subject 92155
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-92155/ses-11022
Running SomMot fcon
0.3996913347236064
Processing 92155's CEST data'
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-90877
Processing subject 90877
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-90877/ses-10907
Running SomMot fcon
0.5206925022524268
Processing 90877's CEST data'
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-112126
Processing subject 112126
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-112126/ses-11157
Running SomMot fcon
0.2218944305592968
Processing 112126's CEST data'
112126_11157
SomMot
[548]
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-89095
Processing subject 89095
/Users/pecsok/Desktop/ImageData/PMACS_remote/data/fmri/postprocessed/7T/xcp_d/sub-89095/ses-11100
Running SomMot fcon
0.397562375744611
Processing 89095's CEST data'
89095_11100
SomMot
[755]
     BBLID Session  sex    age  race  ethnic  dateDiff    SomMot  \
0    19830   10789  2.0  21.92   1.0     2.0       0.0  0.504323   
1    20645   11260  1.0  19.84   2.0     2.0       0.0  0.572062   
2   125511   10906  2.0  20.86   1.0     2.0       0.0       NaN   
3   116019   11135  2.0  25.72   2.0     1.0       0.0  0.476986   
4    19790   10819  2.0  23.37   1.0     2.0       0.0  0.517550   
5    80557   10738  2.0  29.28   2.0     2.0       0.0  0.530501   
6    20642   11261  1.0  25.59   NaN     NaN       0.0  0.474266   
7    20180   11011  1.0  15.43   1.0     2.0      15.0  0.207787   
8   139272   10739  NaN    NaN   NaN     NaN       NaN  0.272185   
9   121085   10851  2.0  20.84   1.0     2.0      14.0  0.439968   
10   93292   10938  2.0  25.32   2.0     2.0       0.0  0.458101   
11   90281   10902  2.0  22.89   1.0     2.0      52.0  0.495047   
12   96659   11096  1.0  17.74   1.0     2.0       0.0  0.363063   
13   93274   10765  2.0  24.64   5.0     2.0      11.0  0.438165   
14  106880   10699  1.0  22.62   2.0     2.0       0.0  0.330849   
15   90077   10962  2.0  21.34   1.0     2.0       0.0  0.276265   
16  121407   10688  2.0  25.78   2.0     2.0       0.0  0.389875   
17  102041   10821  1.0  23.43   1.0     2.0       0.0  0.519093   
18  119791   10705  1.0  21.70   5.0     2.0       0.0  0.371118   
19  106057   11122  2.0  20.12   1.0     2.0       5.0  0.616235   
20   20011   10888  2.0  21.42   1.0     2.0       0.0  0.537007   
21   20543   11259  1.0  21.51   1.0     1.0       0.0  0.562704   
22   87225   10933  NaN    NaN   NaN     NaN       NaN  0.470389   
23   19981   11106  2.0  23.33   2.0     2.0       0.0  0.625642   
24  132641   10692  2.0  23.73   2.0     2.0       0.0  0.631874   
25   88608   10764  1.0  25.36   2.0     2.0       1.0  0.323271   
26   97994   11114  1.0  19.07   1.0     2.0       0.0  0.457556   
27  105979   10791  2.0  24.00   1.0     2.0       0.0  0.372234   
28   92155   11022  NaN    NaN   NaN     NaN       NaN  0.399691   
29   90877   10907  NaN    NaN   NaN     NaN       NaN  0.520693   
30  112126   11157  2.0  21.83   2.0     2.0       0.0  0.221894   
31   89095   11100  2.0  24.62   1.0     2.0       0.0  0.397562   

    avgCEST_SomMot  ctCEST_SomMot    tap_tot  hstatus  
0         7.723693          743.0        NaN       NC  
1         7.390558          639.0        NaN      NaN  
2         7.388057          535.0  104.00000        O  
3         8.048849          506.0  103.33330      PRO  
4         8.941838          598.0        NaN        O  
5         6.923363          731.0   72.33333       NC  
6         8.205851          640.0        NaN      NaN  
7         8.360912          427.0   92.00000      MDD  
8              NaN            NaN        NaN  Unknown  
9         8.731863          683.0        NaN       NC  
10        7.416647          563.0  105.33330      PRO  
11        7.336883          543.0   97.00000       NC  
12             NaN            NaN   95.00000       NC  
13        8.874376          707.0  116.33330      PSY  
14        7.077155          499.0   92.66666       NC  
15        7.627305          718.0   93.33333      PSY  
16        6.892677          716.0  117.00000       NC  
17        8.036966          574.0  103.33330       NC  
18        8.222816          613.0  128.00000      PRO  
19        7.866533          457.0  120.00000      PRO  
20        8.032503          576.0        NaN      PRO  
21        8.078890          723.0        NaN      MDD  
22             NaN            NaN        NaN  Unknown  
23        7.778362          589.0        NaN      NaN  
24        7.681239          720.0   96.33333      PRO  
25        7.964384          680.0  110.00000      PRO  
26        7.400841          668.0  101.66670       NC  
27        7.430732          640.0   92.00000        O  
28             NaN            NaN        NaN  Unknown  
29             NaN            NaN        NaN  Unknown  
30        8.525817          548.0  104.66670       NC  
31        8.374417          755.0  125.66670      MDD

Stage 2: Group Comparisons and Regressions¶

In [ ]:
In [ ]:

Data Trimming¶

In [10]:
# Make a summary table to divide up the cohort by healthy vs psychosis spectrum
if run_grpanalysis:
    grp_df['hstatus'] = grp_df['hstatus'].replace('NC', 'HC')
    grp_df['hstatus'] = grp_df['hstatus'].replace('PROR', 'PSY')
    grp_df['hstatus'] = grp_df['hstatus'].replace('PRO', 'PSY')
    grp_df['hstatus'] = grp_df['hstatus'].replace('S', 'PSY')
    grp_df['hstatus'] = grp_df['hstatus'].replace('O', 'Other')
    grp_df['hstatus'] = grp_df['hstatus'].replace('Unknown', 'Other')
    grp_df['hstatus'] = grp_df['hstatus'].replace('MDD', 'Other')
    
    grp_df = grp_df[grp_df['hstatus'] != 'Other']
    grp_df = grp_df.dropna() #subset=[network, cestcol, 'hstatus']
    
    value_counts = grp_df['hstatus'].value_counts()     
print(grp_df)
print(value_counts)
# Average age (SD)
# Male/female
# Racial demographics
# Psychosis spectrum severity score?
# Medications?
# Comorbidities

     BBLID Session  sex    age  race  ethnic  dateDiff    SomMot  \
3   116019   11135  2.0  25.72   2.0     1.0       0.0  0.476986   
5    80557   10738  2.0  29.28   2.0     2.0       0.0  0.530501   
10   93292   10938  2.0  25.32   2.0     2.0       0.0  0.458101   
11   90281   10902  2.0  22.89   1.0     2.0      52.0  0.495047   
13   93274   10765  2.0  24.64   5.0     2.0      11.0  0.438165   
14  106880   10699  1.0  22.62   2.0     2.0       0.0  0.330849   
15   90077   10962  2.0  21.34   1.0     2.0       0.0  0.276265   
16  121407   10688  2.0  25.78   2.0     2.0       0.0  0.389875   
17  102041   10821  1.0  23.43   1.0     2.0       0.0  0.519093   
18  119791   10705  1.0  21.70   5.0     2.0       0.0  0.371118   
19  106057   11122  2.0  20.12   1.0     2.0       5.0  0.616235   
24  132641   10692  2.0  23.73   2.0     2.0       0.0  0.631874   
25   88608   10764  1.0  25.36   2.0     2.0       1.0  0.323271   
26   97994   11114  1.0  19.07   1.0     2.0       0.0  0.457556   
30  112126   11157  2.0  21.83   2.0     2.0       0.0  0.221894   

    avgCEST_SomMot  ctCEST_SomMot    tap_tot hstatus  
3         8.048849          506.0  103.33330     PSY  
5         6.923363          731.0   72.33333      HC  
10        7.416647          563.0  105.33330     PSY  
11        7.336883          543.0   97.00000      HC  
13        8.874376          707.0  116.33330     PSY  
14        7.077155          499.0   92.66666      HC  
15        7.627305          718.0   93.33333     PSY  
16        6.892677          716.0  117.00000      HC  
17        8.036966          574.0  103.33330      HC  
18        8.222816          613.0  128.00000     PSY  
19        7.866533          457.0  120.00000     PSY  
24        7.681239          720.0   96.33333     PSY  
25        7.964384          680.0  110.00000     PSY  
26        7.400841          668.0  101.66670      HC  
30        8.525817          548.0  104.66670      HC  
hstatus
PSY    8
HC     7
Name: count, dtype: int64
In [ ]:
In [25]:
if run_grpanalysis:
    colors = pd.DataFrame({'Network': ["Cont", "Default", "DorsAttn", "Vis", "SalVentAttn", "SomMot", "Limbic"],
        'Color': ['PuOr', 'PuRd_r', 'PiYG_r', 'PRGn', 'PiYG', 'GnBu_r', 'terrain_r']}) # 
        
    # Create a scatter plot with a linear regression line
    for network in networks:
        cestcol = "avgCEST_" + network
        #graph_df = grp_df[grp_df['hstatus'] != 'Other']
        graph_df = grp_df
        graph_df = graph_df.dropna(subset=[network, cestcol, 'hstatus'])
        # Create a linear regression model for fcon
        color = colors.loc[colors['Network'] == network, 'Color'].values[0]
        sns.set_palette(color)
        plot = sns.lmplot(x=network, y=cestcol, data=graph_df, markers= "x") #hue='hstatus', 
        if network == "SalVentAttn":
            plt.xlabel("SN", fontsize=16)
            plt.ylabel("avgCEST_SN", fontsize=16)
            plt.title('SN FC versus SN GluCEST', fontsize = 20)
        else:
            plt.xlabel(network, fontsize=16)
            plt.ylabel(cestcol, fontsize=16)
            plt.title(network + ' FC versus ' + network + ' GluCEST' , fontsize = 20)
        # Generate and add slope, r2 and p for subset 1
 #       slope, intercept, r_value, p_value, std_err = linregress(graph_df.loc[graph_df['hstatus'] == 'PSY', network], graph_df.loc[graph_df['hstatus'] == 'PSY', cestcol])
 #       plt.text(0.1, 0.8, f'PSY Group\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
   #     # Generate and add slope, r2 and p for subset 2
 #       slope, intercept, r_value, p_value, std_err = linregress(graph_df.loc[graph_df['hstatus'] == 'HC', network], graph_df.loc[graph_df['hstatus'] == 'HC', cestcol])
 #       plt.text(0.4, 0.8, f'HC Group\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
        # Generate and add slope, r2 and p for all data
        slope, intercept, r_value, p_value, std_err = linregress(graph_df[network], graph_df[cestcol])
        plt.text(0.1, 0.8, f'All\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
        plt.show() 

        # Create CNB correlation plot for each network fcon and cest 
        for CNB_score in CNB_scores:
         #   graph_df = grp_df[grp_df['hstatus'] != 'Other']
            graph_df = grp_df
            graph_df = graph_df.dropna(subset=[CNB_score, cestcol, 'hstatus'])
            # Add labels and a title to the plot
            plot = sns.lmplot(x=cestcol, y=CNB_score,data=graph_df) # hue='hstatus',
            if network == "SalVentAttn":
                plt.xlabel("avgCEST_SN", fontsize=16)
                plt.title('SN GluCEST versus ' + CNB_score, fontsize = 20)
            else: 
                plt.xlabel(cestcol, fontsize=16)
                plt.title(network + ' GluCEST versus ' + CNB_score, fontsize = 20)
            plt.ylabel(CNB_score, fontsize=16)
            # Generate and add slope, r2 and p for subset 1
   #         slope, intercept, r_value, p_value, std_err = linregress(graph_df.loc[graph_df['hstatus'] == 'PSY', CNB_score], graph_df.loc[graph_df['hstatus'] == 'PSY', cestcol])
   #         plt.text(0.1, 0.8, f'PSY Group\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
            # Generate and add slope, r2 and p for subset 2
    #        slope, intercept, r_value, p_value, std_err = linregress(graph_df.loc[graph_df['hstatus'] == 'HC', CNB_score], graph_df.loc[graph_df['hstatus'] == 'HC', cestcol])
    #        plt.text(0.4, 0.8, f'HC Group\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
            # Generate and add slope, r2 and p for all data
            slope, intercept, r_value, p_value, std_err = linregress(graph_df[CNB_score], graph_df[cestcol])
            plt.text(0.1, 0.8, f'All\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)   
            # Show the plot
            plt.show()    
            
            #graph_df = grp_df[grp_df['hstatus'] != 'Other']
            graph_df = grp_df
            graph_df = graph_df.dropna(subset=[CNB_score, network, 'hstatus'])
            # Add labels and a title to the plot
            plot = sns.lmplot(x=network, y=CNB_score, data=graph_df, markers= "s") #hue='hstatus',
            if network == "SalVentAttn":
                plt.xlabel("SN", fontsize=16)
                plt.title('SN FC versus ' + CNB_score, fontsize = 20)
            else:
                plt.xlabel(network, fontsize=16)
                plt.title(network + ' FC versus ' + CNB_score, fontsize = 20)
            plt.ylabel(CNB_score, fontsize=16)
            # Generate and add slope, r2 and p for subset 1
  #          slope, intercept, r_value, p_value, std_err = linregress(graph_df.loc[graph_df['hstatus'] == 'PSY', CNB_score], graph_df.loc[graph_df['hstatus'] == 'PSY', network])
  #          plt.text(0.1, 0.8, f'PSY Group\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
            # Generate and add slope, r2 and p for subset 2
   #         slope, intercept, r_value, p_value, std_err = linregress(graph_df.loc[graph_df['hstatus'] == 'HC', CNB_score], graph_df.loc[graph_df['hstatus'] == 'HC', network])
   #         plt.text(0.4, 0.8, f'HC Group\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)
            # Generate and add slope, r2 and p for all data
            slope, intercept, r_value, p_value, std_err = linregress(graph_df[CNB_score], graph_df[network])
            plt.text(0.1, 0.8, f'All\nSlope: {slope:.2f}\nR^2: {r_value**2:.2f}\np: {p_value:.2f}', transform=plt.gca().transAxes)   
            # Show the plot
            plt.show()  
       
        # Make bar graph comparing diagnostic groups
        avg_df = grp_df.groupby('hstatus').agg({cestcol: ['mean', 'std'], network: ['mean', 'std']}).reset_index()
        # Flatten the multi-level columns
        avg_df.columns = ['_'.join(col).strip() for col in avg_df.columns.values]
        print(avg_df)

        # Plot CEST bar graph with error bars
        sns.barplot(x='hstatus_', y=cestcol + '_mean', data=avg_df, yerr=avg_df[cestcol + '_std'], label='CEST')
        plt.xlabel('Diagnostic Group')
        plt.ylabel('Mean Value')
        plt.ylim(5, 9.5)
        plt.title('Average CEST for each hstatus group')
        plt.legend()
        plt.show()

        # Plot fcon bar graph with error bars
        sns.barplot(x='hstatus_', y=network + '_mean', data=avg_df, yerr=avg_df[network + '_std'], label='fcon')
        plt.xlabel('Diagnostic Group')
        plt.ylabel('Mean Value')
        plt.title('Average fcon for each hstatus group')
        plt.legend()
        plt.show()

        

hstatus
HC       10
Other    10
PSY       9
Name: count, dtype: int64
hstatus
HC       10
Other    10
PSY       9
Name: count, dtype: int64
     BBLID Session  sex    age  race  ethnic  dateDiff      Cont   Default  \
0    19830   10789  2.0  21.92   1.0     2.0       0.0  0.175604  0.376170   
1    20645   11260  1.0  19.84   2.0     2.0       0.0       NaN  0.210973   
2   125511   10906  2.0  20.86   1.0     2.0       0.0       NaN  0.260692   
3   116019   11135  2.0  25.72   2.0     1.0       0.0  0.121458  0.322248   
4    19790   10819  2.0  23.37   1.0     2.0       0.0  0.248519  0.331945   
5    80557   10738  2.0  29.28   2.0     2.0       0.0       NaN       NaN   
6    20642   11261  1.0  25.59   NaN     NaN       0.0  0.232299  0.216163   
7    20180   11011  1.0  15.43   1.0     2.0      15.0  0.096672  0.101490   
8   139272   10739  NaN    NaN   NaN     NaN       NaN  0.085998  0.153989   
9   121085   10851  2.0  20.84   1.0     2.0      14.0  0.201904  0.159492   
10   93292   10938  2.0  25.32   2.0     2.0       0.0  0.246400  0.168437   
11   90281   10902  2.0  22.89   1.0     2.0      52.0       NaN  0.235045   
12   96659   11096  1.0  17.74   1.0     2.0       0.0  0.220468  0.291296   
13   93274   10765  2.0  24.64   5.0     2.0      11.0  0.222760  0.233656   
14  106880   10699  1.0  22.62   2.0     2.0       0.0  0.341071  0.069097   
15   90077   10962  2.0  21.34   1.0     2.0       0.0  0.165071  0.196767   
16  121407   10688  2.0  25.78   2.0     2.0       0.0  0.169243  0.402548   
17  102041   10821  1.0  23.43   1.0     2.0       0.0  0.261861  0.323492   
18  119791   10705  1.0  21.70   5.0     2.0       0.0  0.231845  0.195510   
19  106057   11122  2.0  20.12   1.0     2.0       5.0  0.201449  0.240199   
20   20011   10888  2.0  21.42   1.0     2.0       0.0  0.259585  0.281621   
21   20543   11259  1.0  21.51   1.0     1.0       0.0  0.218721  0.579795   
22   87225   10933  NaN    NaN   NaN     NaN       NaN       NaN  0.230372   
23   19981   11106  2.0  23.33   2.0     2.0       0.0  0.150234  0.321884   
24  132641   10692  2.0  23.73   2.0     2.0       0.0  0.233773       NaN   
25   88608   10764  1.0  25.36   2.0     2.0       1.0  0.201716  0.156970   
26   97994   11114  1.0  19.07   1.0     2.0       0.0  0.243740  0.267923   
27  105979   10791  2.0  24.00   1.0     2.0       0.0  0.119005  0.399663   
28   92155   11022  NaN    NaN   NaN     NaN       NaN  0.125406  0.235575   
29   90877   10907  NaN    NaN   NaN     NaN       NaN  0.114077  0.270397   
30  112126   11157  2.0  21.83   2.0     2.0       0.0  0.102177  0.097644   
31   89095   11100  2.0  24.62   1.0     2.0       0.0  0.128059  0.364319   

    DorsAttn  ...  avgCEST_Vis  ctCEST_Vis  avgCEST_Limbic  ctCEST_Limbic  \
0   0.402285  ...    14.297329       175.0        8.867364          391.0   
1   0.257396  ...    10.610277       122.0        8.370049          258.0   
2   0.258377  ...     8.967306        46.0       10.076208           62.0   
3   0.287490  ...     9.610587       194.0        5.855585          267.0   
4   0.520766  ...     8.387602       214.0        6.432389          304.0   
5   0.316759  ...    12.741945       147.0       10.419856          189.0   
6   0.371144  ...     8.465859       293.0        3.779353          162.0   
7   0.063928  ...     9.343160       153.0        5.411261          236.0   
8   0.241768  ...          NaN         NaN             NaN            NaN   
9   0.292959  ...    13.178692       110.0        6.876791          116.0   
10  0.243422  ...     9.746861       184.0        7.552845          168.0   
11  0.429669  ...    10.589238       300.0        6.771436          247.0   
12  0.238394  ...          NaN         NaN             NaN            NaN   
13  0.278428  ...    12.802977       294.0        6.410949          320.0   
14  0.176538  ...    13.682704       292.0        5.676007          220.0   
15  0.330240  ...     9.176037       161.0        4.421310          533.0   
16  0.309288  ...     9.737379        21.0        5.850790          187.0   
17  0.510831  ...    10.190264       154.0        6.229536          279.0   
18  0.200455  ...     9.714172       242.0        6.640281          229.0   
19  0.335207  ...    10.088696       125.0        7.649896          287.0   
20  0.294042  ...    11.131604       152.0        7.082764          264.0   
21  0.479209  ...     9.479773        27.0        6.994899          147.0   
22  0.286440  ...          NaN         NaN             NaN            NaN   
23  0.218326  ...    13.034999       138.0        5.861445          411.0   
24  0.401307  ...    10.633214       128.0        7.509117          298.0   
25  0.219597  ...    12.498565       146.0        7.559701          369.0   
26  0.494674  ...     7.216132       190.0        4.831669          114.0   
27  0.631075  ...    12.373299       162.0        7.654502          258.0   
28  0.306728  ...          NaN         NaN             NaN            NaN   
29  0.251416  ...          NaN         NaN             NaN            NaN   
30  0.313409  ...    13.772174       114.0        8.371397           73.0   
31  0.473277  ...    12.511567       140.0        6.177131           95.0   

    avgCEST_SalVentAttn  ctCEST_SalVentAttn    tap_tot  er40_cr    medf_pc  \
0              8.034846              2143.0        NaN     39.0        NaN   
1              7.546548              1753.0        NaN      NaN        NaN   
2              7.277624              1245.0  104.00000     38.0  88.888889   
3              7.863291              1068.0  103.33330     38.0  77.777778   
4              8.805663              1815.0        NaN     39.0        NaN   
5              7.181154              1830.0   72.33333      NaN  91.666667   
6              8.565783              1634.0        NaN      NaN        NaN   
7              8.124917              1393.0   92.00000     34.0  69.444444   
8                   NaN                 NaN        NaN      NaN        NaN   
9              8.867149              1667.0        NaN      NaN        NaN   
10             7.937581              1527.0  105.33330     39.0  77.777778   
11             7.662420              1683.0   97.00000     39.0  66.666667   
12                  NaN                 NaN   95.00000     40.0  83.333333   
13             8.939010              1862.0  116.33330     37.0  69.444444   
14             7.325671              1855.0   92.66666      NaN  72.222222   
15             7.404367              1885.0   93.33333     37.0  80.555556   
16             7.365407              1667.0  117.00000     37.0  86.111111   
17             7.774126              1705.0  103.33330     36.0  86.111111   
18             8.103207              2062.0  128.00000     38.0  66.666667   
19             7.989862              1001.0  120.00000     38.0  91.666667   
20             8.076560              1678.0        NaN      NaN        NaN   
21             8.118948              1817.0        NaN      NaN        NaN   
22                  NaN                 NaN        NaN      NaN        NaN   
23             8.168039              1530.0        NaN      NaN        NaN   
24             7.920916              1806.0   96.33333     40.0  69.444444   
25             8.152667              1849.0  110.00000     38.0  88.888889   
26             7.594341              1556.0  101.66670     38.0  75.000000   
27             8.100647              1514.0   92.00000     40.0  80.555556   
28                  NaN                 NaN        NaN      NaN        NaN   
29                  NaN                 NaN        NaN      NaN        NaN   
30             8.358057              1331.0  104.66670     39.0  83.333333   
31             8.547472              1716.0  125.66670     36.0  86.111111   

    hstatus  
0        HC  
1       NaN  
2     Other  
3       PSY  
4     Other  
5        HC  
6       NaN  
7     Other  
8     Other  
9        HC  
10      PSY  
11       HC  
12       HC  
13      PSY  
14       HC  
15      PSY  
16       HC  
17       HC  
18      PSY  
19      PSY  
20      PSY  
21    Other  
22    Other  
23      NaN  
24      PSY  
25      PSY  
26       HC  
27    Other  
28    Other  
29    Other  
30       HC  
31    Other  

[32 rows x 32 columns]

  hstatus_  avgCEST_Cont_mean  avgCEST_Cont_std  Cont_mean  Cont_std
0       HC           8.185614          0.499726   0.214508  0.071204
1    Other           8.067074          0.188945   0.142057  0.058799
2      PSY           8.444832          0.392330   0.209340  0.043280

     BBLID Session  sex    age  race  ethnic  dateDiff      Cont   Default  \
0    19830   10789  2.0  21.92   1.0     2.0       0.0  0.175604  0.376170   
1    20645   11260  1.0  19.84   2.0     2.0       0.0       NaN  0.210973   
2   125511   10906  2.0  20.86   1.0     2.0       0.0       NaN  0.260692   
3   116019   11135  2.0  25.72   2.0     1.0       0.0  0.121458  0.322248   
4    19790   10819  2.0  23.37   1.0     2.0       0.0  0.248519  0.331945   
5    80557   10738  2.0  29.28   2.0     2.0       0.0       NaN       NaN   
6    20642   11261  1.0  25.59   NaN     NaN       0.0  0.232299  0.216163   
7    20180   11011  1.0  15.43   1.0     2.0      15.0  0.096672  0.101490   
8   139272   10739  NaN    NaN   NaN     NaN       NaN  0.085998  0.153989   
9   121085   10851  2.0  20.84   1.0     2.0      14.0  0.201904  0.159492   
10   93292   10938  2.0  25.32   2.0     2.0       0.0  0.246400  0.168437   
11   90281   10902  2.0  22.89   1.0     2.0      52.0       NaN  0.235045   
12   96659   11096  1.0  17.74   1.0     2.0       0.0  0.220468  0.291296   
13   93274   10765  2.0  24.64   5.0     2.0      11.0  0.222760  0.233656   
14  106880   10699  1.0  22.62   2.0     2.0       0.0  0.341071  0.069097   
15   90077   10962  2.0  21.34   1.0     2.0       0.0  0.165071  0.196767   
16  121407   10688  2.0  25.78   2.0     2.0       0.0  0.169243  0.402548   
17  102041   10821  1.0  23.43   1.0     2.0       0.0  0.261861  0.323492   
18  119791   10705  1.0  21.70   5.0     2.0       0.0  0.231845  0.195510   
19  106057   11122  2.0  20.12   1.0     2.0       5.0  0.201449  0.240199   
20   20011   10888  2.0  21.42   1.0     2.0       0.0  0.259585  0.281621   
21   20543   11259  1.0  21.51   1.0     1.0       0.0  0.218721  0.579795   
22   87225   10933  NaN    NaN   NaN     NaN       NaN       NaN  0.230372   
23   19981   11106  2.0  23.33   2.0     2.0       0.0  0.150234  0.321884   
24  132641   10692  2.0  23.73   2.0     2.0       0.0  0.233773       NaN   
25   88608   10764  1.0  25.36   2.0     2.0       1.0  0.201716  0.156970   
26   97994   11114  1.0  19.07   1.0     2.0       0.0  0.243740  0.267923   
27  105979   10791  2.0  24.00   1.0     2.0       0.0  0.119005  0.399663   
28   92155   11022  NaN    NaN   NaN     NaN       NaN  0.125406  0.235575   
29   90877   10907  NaN    NaN   NaN     NaN       NaN  0.114077  0.270397   
30  112126   11157  2.0  21.83   2.0     2.0       0.0  0.102177  0.097644   
31   89095   11100  2.0  24.62   1.0     2.0       0.0  0.128059  0.364319   

    DorsAttn  ...  avgCEST_Vis  ctCEST_Vis  avgCEST_Limbic  ctCEST_Limbic  \
0   0.402285  ...    14.297329       175.0        8.867364          391.0   
1   0.257396  ...    10.610277       122.0        8.370049          258.0   
2   0.258377  ...     8.967306        46.0       10.076208           62.0   
3   0.287490  ...     9.610587       194.0        5.855585          267.0   
4   0.520766  ...     8.387602       214.0        6.432389          304.0   
5   0.316759  ...    12.741945       147.0       10.419856          189.0   
6   0.371144  ...     8.465859       293.0        3.779353          162.0   
7   0.063928  ...     9.343160       153.0        5.411261          236.0   
8   0.241768  ...          NaN         NaN             NaN            NaN   
9   0.292959  ...    13.178692       110.0        6.876791          116.0   
10  0.243422  ...     9.746861       184.0        7.552845          168.0   
11  0.429669  ...    10.589238       300.0        6.771436          247.0   
12  0.238394  ...          NaN         NaN             NaN            NaN   
13  0.278428  ...    12.802977       294.0        6.410949          320.0   
14  0.176538  ...    13.682704       292.0        5.676007          220.0   
15  0.330240  ...     9.176037       161.0        4.421310          533.0   
16  0.309288  ...     9.737379        21.0        5.850790          187.0   
17  0.510831  ...    10.190264       154.0        6.229536          279.0   
18  0.200455  ...     9.714172       242.0        6.640281          229.0   
19  0.335207  ...    10.088696       125.0        7.649896          287.0   
20  0.294042  ...    11.131604       152.0        7.082764          264.0   
21  0.479209  ...     9.479773        27.0        6.994899          147.0   
22  0.286440  ...          NaN         NaN             NaN            NaN   
23  0.218326  ...    13.034999       138.0        5.861445          411.0   
24  0.401307  ...    10.633214       128.0        7.509117          298.0   
25  0.219597  ...    12.498565       146.0        7.559701          369.0   
26  0.494674  ...     7.216132       190.0        4.831669          114.0   
27  0.631075  ...    12.373299       162.0        7.654502          258.0   
28  0.306728  ...          NaN         NaN             NaN            NaN   
29  0.251416  ...          NaN         NaN             NaN            NaN   
30  0.313409  ...    13.772174       114.0        8.371397           73.0   
31  0.473277  ...    12.511567       140.0        6.177131           95.0   

    avgCEST_SalVentAttn  ctCEST_SalVentAttn    tap_tot  er40_cr    medf_pc  \
0              8.034846              2143.0        NaN     39.0        NaN   
1              7.546548              1753.0        NaN      NaN        NaN   
2              7.277624              1245.0  104.00000     38.0  88.888889   
3              7.863291              1068.0  103.33330     38.0  77.777778   
4              8.805663              1815.0        NaN     39.0        NaN   
5              7.181154              1830.0   72.33333      NaN  91.666667   
6              8.565783              1634.0        NaN      NaN        NaN   
7              8.124917              1393.0   92.00000     34.0  69.444444   
8                   NaN                 NaN        NaN      NaN        NaN   
9              8.867149              1667.0        NaN      NaN        NaN   
10             7.937581              1527.0  105.33330     39.0  77.777778   
11             7.662420              1683.0   97.00000     39.0  66.666667   
12                  NaN                 NaN   95.00000     40.0  83.333333   
13             8.939010              1862.0  116.33330     37.0  69.444444   
14             7.325671              1855.0   92.66666      NaN  72.222222   
15             7.404367              1885.0   93.33333     37.0  80.555556   
16             7.365407              1667.0  117.00000     37.0  86.111111   
17             7.774126              1705.0  103.33330     36.0  86.111111   
18             8.103207              2062.0  128.00000     38.0  66.666667   
19             7.989862              1001.0  120.00000     38.0  91.666667   
20             8.076560              1678.0        NaN      NaN        NaN   
21             8.118948              1817.0        NaN      NaN        NaN   
22                  NaN                 NaN        NaN      NaN        NaN   
23             8.168039              1530.0        NaN      NaN        NaN   
24             7.920916              1806.0   96.33333     40.0  69.444444   
25             8.152667              1849.0  110.00000     38.0  88.888889   
26             7.594341              1556.0  101.66670     38.0  75.000000   
27             8.100647              1514.0   92.00000     40.0  80.555556   
28                  NaN                 NaN        NaN      NaN        NaN   
29                  NaN                 NaN        NaN      NaN        NaN   
30             8.358057              1331.0  104.66670     39.0  83.333333   
31             8.547472              1716.0  125.66670     36.0  86.111111   

    hstatus  
0        HC  
1       NaN  
2     Other  
3       PSY  
4     Other  
5        HC  
6       NaN  
7     Other  
8     Other  
9        HC  
10      PSY  
11       HC  
12       HC  
13      PSY  
14       HC  
15      PSY  
16       HC  
17       HC  
18      PSY  
19      PSY  
20      PSY  
21    Other  
22    Other  
23      NaN  
24      PSY  
25      PSY  
26       HC  
27    Other  
28    Other  
29    Other  
30       HC  
31    Other  

[32 rows x 32 columns]

  hstatus_  avgCEST_Default_mean  avgCEST_Default_std  Default_mean  \
0       HC              7.823633             0.416928      0.246967   
1    Other              7.742404             0.405308      0.292824   
2      PSY              7.817142             0.321882      0.224426   

   Default_std  
0     0.117733  
1     0.135403  
2     0.056645  

     BBLID Session  sex    age  race  ethnic  dateDiff      Cont   Default  \
0    19830   10789  2.0  21.92   1.0     2.0       0.0  0.175604  0.376170   
1    20645   11260  1.0  19.84   2.0     2.0       0.0       NaN  0.210973   
2   125511   10906  2.0  20.86   1.0     2.0       0.0       NaN  0.260692   
3   116019   11135  2.0  25.72   2.0     1.0       0.0  0.121458  0.322248   
4    19790   10819  2.0  23.37   1.0     2.0       0.0  0.248519  0.331945   
5    80557   10738  2.0  29.28   2.0     2.0       0.0       NaN       NaN   
6    20642   11261  1.0  25.59   NaN     NaN       0.0  0.232299  0.216163   
7    20180   11011  1.0  15.43   1.0     2.0      15.0  0.096672  0.101490   
8   139272   10739  NaN    NaN   NaN     NaN       NaN  0.085998  0.153989   
9   121085   10851  2.0  20.84   1.0     2.0      14.0  0.201904  0.159492   
10   93292   10938  2.0  25.32   2.0     2.0       0.0  0.246400  0.168437   
11   90281   10902  2.0  22.89   1.0     2.0      52.0       NaN  0.235045   
12   96659   11096  1.0  17.74   1.0     2.0       0.0  0.220468  0.291296   
13   93274   10765  2.0  24.64   5.0     2.0      11.0  0.222760  0.233656   
14  106880   10699  1.0  22.62   2.0     2.0       0.0  0.341071  0.069097   
15   90077   10962  2.0  21.34   1.0     2.0       0.0  0.165071  0.196767   
16  121407   10688  2.0  25.78   2.0     2.0       0.0  0.169243  0.402548   
17  102041   10821  1.0  23.43   1.0     2.0       0.0  0.261861  0.323492   
18  119791   10705  1.0  21.70   5.0     2.0       0.0  0.231845  0.195510   
19  106057   11122  2.0  20.12   1.0     2.0       5.0  0.201449  0.240199   
20   20011   10888  2.0  21.42   1.0     2.0       0.0  0.259585  0.281621   
21   20543   11259  1.0  21.51   1.0     1.0       0.0  0.218721  0.579795   
22   87225   10933  NaN    NaN   NaN     NaN       NaN       NaN  0.230372   
23   19981   11106  2.0  23.33   2.0     2.0       0.0  0.150234  0.321884   
24  132641   10692  2.0  23.73   2.0     2.0       0.0  0.233773       NaN   
25   88608   10764  1.0  25.36   2.0     2.0       1.0  0.201716  0.156970   
26   97994   11114  1.0  19.07   1.0     2.0       0.0  0.243740  0.267923   
27  105979   10791  2.0  24.00   1.0     2.0       0.0  0.119005  0.399663   
28   92155   11022  NaN    NaN   NaN     NaN       NaN  0.125406  0.235575   
29   90877   10907  NaN    NaN   NaN     NaN       NaN  0.114077  0.270397   
30  112126   11157  2.0  21.83   2.0     2.0       0.0  0.102177  0.097644   
31   89095   11100  2.0  24.62   1.0     2.0       0.0  0.128059  0.364319   

    DorsAttn  ...  avgCEST_Vis  ctCEST_Vis  avgCEST_Limbic  ctCEST_Limbic  \
0   0.402285  ...    14.297329       175.0        8.867364          391.0   
1   0.257396  ...    10.610277       122.0        8.370049          258.0   
2   0.258377  ...     8.967306        46.0       10.076208           62.0   
3   0.287490  ...     9.610587       194.0        5.855585          267.0   
4   0.520766  ...     8.387602       214.0        6.432389          304.0   
5   0.316759  ...    12.741945       147.0       10.419856          189.0   
6   0.371144  ...     8.465859       293.0        3.779353          162.0   
7   0.063928  ...     9.343160       153.0        5.411261          236.0   
8   0.241768  ...          NaN         NaN             NaN            NaN   
9   0.292959  ...    13.178692       110.0        6.876791          116.0   
10  0.243422  ...     9.746861       184.0        7.552845          168.0   
11  0.429669  ...    10.589238       300.0        6.771436          247.0   
12  0.238394  ...          NaN         NaN             NaN            NaN   
13  0.278428  ...    12.802977       294.0        6.410949          320.0   
14  0.176538  ...    13.682704       292.0        5.676007          220.0   
15  0.330240  ...     9.176037       161.0        4.421310          533.0   
16  0.309288  ...     9.737379        21.0        5.850790          187.0   
17  0.510831  ...    10.190264       154.0        6.229536          279.0   
18  0.200455  ...     9.714172       242.0        6.640281          229.0   
19  0.335207  ...    10.088696       125.0        7.649896          287.0   
20  0.294042  ...    11.131604       152.0        7.082764          264.0   
21  0.479209  ...     9.479773        27.0        6.994899          147.0   
22  0.286440  ...          NaN         NaN             NaN            NaN   
23  0.218326  ...    13.034999       138.0        5.861445          411.0   
24  0.401307  ...    10.633214       128.0        7.509117          298.0   
25  0.219597  ...    12.498565       146.0        7.559701          369.0   
26  0.494674  ...     7.216132       190.0        4.831669          114.0   
27  0.631075  ...    12.373299       162.0        7.654502          258.0   
28  0.306728  ...          NaN         NaN             NaN            NaN   
29  0.251416  ...          NaN         NaN             NaN            NaN   
30  0.313409  ...    13.772174       114.0        8.371397           73.0   
31  0.473277  ...    12.511567       140.0        6.177131           95.0   

    avgCEST_SalVentAttn  ctCEST_SalVentAttn    tap_tot  er40_cr    medf_pc  \
0              8.034846              2143.0        NaN     39.0        NaN   
1              7.546548              1753.0        NaN      NaN        NaN   
2              7.277624              1245.0  104.00000     38.0  88.888889   
3              7.863291              1068.0  103.33330     38.0  77.777778   
4              8.805663              1815.0        NaN     39.0        NaN   
5              7.181154              1830.0   72.33333      NaN  91.666667   
6              8.565783              1634.0        NaN      NaN        NaN   
7              8.124917              1393.0   92.00000     34.0  69.444444   
8                   NaN                 NaN        NaN      NaN        NaN   
9              8.867149              1667.0        NaN      NaN        NaN   
10             7.937581              1527.0  105.33330     39.0  77.777778   
11             7.662420              1683.0   97.00000     39.0  66.666667   
12                  NaN                 NaN   95.00000     40.0  83.333333   
13             8.939010              1862.0  116.33330     37.0  69.444444   
14             7.325671              1855.0   92.66666      NaN  72.222222   
15             7.404367              1885.0   93.33333     37.0  80.555556   
16             7.365407              1667.0  117.00000     37.0  86.111111   
17             7.774126              1705.0  103.33330     36.0  86.111111   
18             8.103207              2062.0  128.00000     38.0  66.666667   
19             7.989862              1001.0  120.00000     38.0  91.666667   
20             8.076560              1678.0        NaN      NaN        NaN   
21             8.118948              1817.0        NaN      NaN        NaN   
22                  NaN                 NaN        NaN      NaN        NaN   
23             8.168039              1530.0        NaN      NaN        NaN   
24             7.920916              1806.0   96.33333     40.0  69.444444   
25             8.152667              1849.0  110.00000     38.0  88.888889   
26             7.594341              1556.0  101.66670     38.0  75.000000   
27             8.100647              1514.0   92.00000     40.0  80.555556   
28                  NaN                 NaN        NaN      NaN        NaN   
29                  NaN                 NaN        NaN      NaN        NaN   
30             8.358057              1331.0  104.66670     39.0  83.333333   
31             8.547472              1716.0  125.66670     36.0  86.111111   

    hstatus  
0        HC  
1       NaN  
2     Other  
3       PSY  
4     Other  
5        HC  
6       NaN  
7     Other  
8     Other  
9        HC  
10      PSY  
11       HC  
12       HC  
13      PSY  
14       HC  
15      PSY  
16       HC  
17       HC  
18      PSY  
19      PSY  
20      PSY  
21    Other  
22    Other  
23      NaN  
24      PSY  
25      PSY  
26       HC  
27    Other  
28    Other  
29    Other  
30       HC  
31    Other  

[32 rows x 32 columns]

  hstatus_  avgCEST_DorsAttn_mean  avgCEST_DorsAttn_std  DorsAttn_mean  \
0       HC               7.531305              0.721082       0.348481   
1    Other               8.203656              0.968130       0.351299   
2      PSY               7.981276              1.204224       0.287799   

   DorsAttn_std  
0      0.108400  
1      0.169187  
2      0.062560  

     BBLID Session  sex    age  race  ethnic  dateDiff      Cont   Default  \
0    19830   10789  2.0  21.92   1.0     2.0       0.0  0.175604  0.376170   
1    20645   11260  1.0  19.84   2.0     2.0       0.0       NaN  0.210973   
2   125511   10906  2.0  20.86   1.0     2.0       0.0       NaN  0.260692   
3   116019   11135  2.0  25.72   2.0     1.0       0.0  0.121458  0.322248   
4    19790   10819  2.0  23.37   1.0     2.0       0.0  0.248519  0.331945   
5    80557   10738  2.0  29.28   2.0     2.0       0.0       NaN       NaN   
6    20642   11261  1.0  25.59   NaN     NaN       0.0  0.232299  0.216163   
7    20180   11011  1.0  15.43   1.0     2.0      15.0  0.096672  0.101490   
8   139272   10739  NaN    NaN   NaN     NaN       NaN  0.085998  0.153989   
9   121085   10851  2.0  20.84   1.0     2.0      14.0  0.201904  0.159492   
10   93292   10938  2.0  25.32   2.0     2.0       0.0  0.246400  0.168437   
11   90281   10902  2.0  22.89   1.0     2.0      52.0       NaN  0.235045   
12   96659   11096  1.0  17.74   1.0     2.0       0.0  0.220468  0.291296   
13   93274   10765  2.0  24.64   5.0     2.0      11.0  0.222760  0.233656   
14  106880   10699  1.0  22.62   2.0     2.0       0.0  0.341071  0.069097   
15   90077   10962  2.0  21.34   1.0     2.0       0.0  0.165071  0.196767   
16  121407   10688  2.0  25.78   2.0     2.0       0.0  0.169243  0.402548   
17  102041   10821  1.0  23.43   1.0     2.0       0.0  0.261861  0.323492   
18  119791   10705  1.0  21.70   5.0     2.0       0.0  0.231845  0.195510   
19  106057   11122  2.0  20.12   1.0     2.0       5.0  0.201449  0.240199   
20   20011   10888  2.0  21.42   1.0     2.0       0.0  0.259585  0.281621   
21   20543   11259  1.0  21.51   1.0     1.0       0.0  0.218721  0.579795   
22   87225   10933  NaN    NaN   NaN     NaN       NaN       NaN  0.230372   
23   19981   11106  2.0  23.33   2.0     2.0       0.0  0.150234  0.321884   
24  132641   10692  2.0  23.73   2.0     2.0       0.0  0.233773       NaN   
25   88608   10764  1.0  25.36   2.0     2.0       1.0  0.201716  0.156970   
26   97994   11114  1.0  19.07   1.0     2.0       0.0  0.243740  0.267923   
27  105979   10791  2.0  24.00   1.0     2.0       0.0  0.119005  0.399663   
28   92155   11022  NaN    NaN   NaN     NaN       NaN  0.125406  0.235575   
29   90877   10907  NaN    NaN   NaN     NaN       NaN  0.114077  0.270397   
30  112126   11157  2.0  21.83   2.0     2.0       0.0  0.102177  0.097644   
31   89095   11100  2.0  24.62   1.0     2.0       0.0  0.128059  0.364319   

    DorsAttn  ...  avgCEST_Vis  ctCEST_Vis  avgCEST_Limbic  ctCEST_Limbic  \
0   0.402285  ...    14.297329       175.0        8.867364          391.0   
1   0.257396  ...    10.610277       122.0        8.370049          258.0   
2   0.258377  ...     8.967306        46.0       10.076208           62.0   
3   0.287490  ...     9.610587       194.0        5.855585          267.0   
4   0.520766  ...     8.387602       214.0        6.432389          304.0   
5   0.316759  ...    12.741945       147.0       10.419856          189.0   
6   0.371144  ...     8.465859       293.0        3.779353          162.0   
7   0.063928  ...     9.343160       153.0        5.411261          236.0   
8   0.241768  ...          NaN         NaN             NaN            NaN   
9   0.292959  ...    13.178692       110.0        6.876791          116.0   
10  0.243422  ...     9.746861       184.0        7.552845          168.0   
11  0.429669  ...    10.589238       300.0        6.771436          247.0   
12  0.238394  ...          NaN         NaN             NaN            NaN   
13  0.278428  ...    12.802977       294.0        6.410949          320.0   
14  0.176538  ...    13.682704       292.0        5.676007          220.0   
15  0.330240  ...     9.176037       161.0        4.421310          533.0   
16  0.309288  ...     9.737379        21.0        5.850790          187.0   
17  0.510831  ...    10.190264       154.0        6.229536          279.0   
18  0.200455  ...     9.714172       242.0        6.640281          229.0   
19  0.335207  ...    10.088696       125.0        7.649896          287.0   
20  0.294042  ...    11.131604       152.0        7.082764          264.0   
21  0.479209  ...     9.479773        27.0        6.994899          147.0   
22  0.286440  ...          NaN         NaN             NaN            NaN   
23  0.218326  ...    13.034999       138.0        5.861445          411.0   
24  0.401307  ...    10.633214       128.0        7.509117          298.0   
25  0.219597  ...    12.498565       146.0        7.559701          369.0   
26  0.494674  ...     7.216132       190.0        4.831669          114.0   
27  0.631075  ...    12.373299       162.0        7.654502          258.0   
28  0.306728  ...          NaN         NaN             NaN            NaN   
29  0.251416  ...          NaN         NaN             NaN            NaN   
30  0.313409  ...    13.772174       114.0        8.371397           73.0   
31  0.473277  ...    12.511567       140.0        6.177131           95.0   

    avgCEST_SalVentAttn  ctCEST_SalVentAttn    tap_tot  er40_cr    medf_pc  \
0              8.034846              2143.0        NaN     39.0        NaN   
1              7.546548              1753.0        NaN      NaN        NaN   
2              7.277624              1245.0  104.00000     38.0  88.888889   
3              7.863291              1068.0  103.33330     38.0  77.777778   
4              8.805663              1815.0        NaN     39.0        NaN   
5              7.181154              1830.0   72.33333      NaN  91.666667   
6              8.565783              1634.0        NaN      NaN        NaN   
7              8.124917              1393.0   92.00000     34.0  69.444444   
8                   NaN                 NaN        NaN      NaN        NaN   
9              8.867149              1667.0        NaN      NaN        NaN   
10             7.937581              1527.0  105.33330     39.0  77.777778   
11             7.662420              1683.0   97.00000     39.0  66.666667   
12                  NaN                 NaN   95.00000     40.0  83.333333   
13             8.939010              1862.0  116.33330     37.0  69.444444   
14             7.325671              1855.0   92.66666      NaN  72.222222   
15             7.404367              1885.0   93.33333     37.0  80.555556   
16             7.365407              1667.0  117.00000     37.0  86.111111   
17             7.774126              1705.0  103.33330     36.0  86.111111   
18             8.103207              2062.0  128.00000     38.0  66.666667   
19             7.989862              1001.0  120.00000     38.0  91.666667   
20             8.076560              1678.0        NaN      NaN        NaN   
21             8.118948              1817.0        NaN      NaN        NaN   
22                  NaN                 NaN        NaN      NaN        NaN   
23             8.168039              1530.0        NaN      NaN        NaN   
24             7.920916              1806.0   96.33333     40.0  69.444444   
25             8.152667              1849.0  110.00000     38.0  88.888889   
26             7.594341              1556.0  101.66670     38.0  75.000000   
27             8.100647              1514.0   92.00000     40.0  80.555556   
28                  NaN                 NaN        NaN      NaN        NaN   
29                  NaN                 NaN        NaN      NaN        NaN   
30             8.358057              1331.0  104.66670     39.0  83.333333   
31             8.547472              1716.0  125.66670     36.0  86.111111   

    hstatus  
0        HC  
1       NaN  
2     Other  
3       PSY  
4     Other  
5        HC  
6       NaN  
7     Other  
8     Other  
9        HC  
10      PSY  
11       HC  
12       HC  
13      PSY  
14       HC  
15      PSY  
16       HC  
17       HC  
18      PSY  
19      PSY  
20      PSY  
21    Other  
22    Other  
23      NaN  
24      PSY  
25      PSY  
26       HC  
27    Other  
28    Other  
29    Other  
30       HC  
31    Other  

[32 rows x 32 columns]

  hstatus_  avgCEST_Vis_mean  avgCEST_Vis_std  Vis_mean   Vis_std
0       HC         11.711762         2.390401  0.448846  0.207738
1    Other         10.177118         1.795586  0.565973  0.185518
2      PSY         10.600301         1.300093  0.516471  0.167178

     BBLID Session  sex    age  race  ethnic  dateDiff      Cont   Default  \
0    19830   10789  2.0  21.92   1.0     2.0       0.0  0.175604  0.376170   
1    20645   11260  1.0  19.84   2.0     2.0       0.0       NaN  0.210973   
2   125511   10906  2.0  20.86   1.0     2.0       0.0       NaN  0.260692   
3   116019   11135  2.0  25.72   2.0     1.0       0.0  0.121458  0.322248   
4    19790   10819  2.0  23.37   1.0     2.0       0.0  0.248519  0.331945   
5    80557   10738  2.0  29.28   2.0     2.0       0.0       NaN       NaN   
6    20642   11261  1.0  25.59   NaN     NaN       0.0  0.232299  0.216163   
7    20180   11011  1.0  15.43   1.0     2.0      15.0  0.096672  0.101490   
8   139272   10739  NaN    NaN   NaN     NaN       NaN  0.085998  0.153989   
9   121085   10851  2.0  20.84   1.0     2.0      14.0  0.201904  0.159492   
10   93292   10938  2.0  25.32   2.0     2.0       0.0  0.246400  0.168437   
11   90281   10902  2.0  22.89   1.0     2.0      52.0       NaN  0.235045   
12   96659   11096  1.0  17.74   1.0     2.0       0.0  0.220468  0.291296   
13   93274   10765  2.0  24.64   5.0     2.0      11.0  0.222760  0.233656   
14  106880   10699  1.0  22.62   2.0     2.0       0.0  0.341071  0.069097   
15   90077   10962  2.0  21.34   1.0     2.0       0.0  0.165071  0.196767   
16  121407   10688  2.0  25.78   2.0     2.0       0.0  0.169243  0.402548   
17  102041   10821  1.0  23.43   1.0     2.0       0.0  0.261861  0.323492   
18  119791   10705  1.0  21.70   5.0     2.0       0.0  0.231845  0.195510   
19  106057   11122  2.0  20.12   1.0     2.0       5.0  0.201449  0.240199   
20   20011   10888  2.0  21.42   1.0     2.0       0.0  0.259585  0.281621   
21   20543   11259  1.0  21.51   1.0     1.0       0.0  0.218721  0.579795   
22   87225   10933  NaN    NaN   NaN     NaN       NaN       NaN  0.230372   
23   19981   11106  2.0  23.33   2.0     2.0       0.0  0.150234  0.321884   
24  132641   10692  2.0  23.73   2.0     2.0       0.0  0.233773       NaN   
25   88608   10764  1.0  25.36   2.0     2.0       1.0  0.201716  0.156970   
26   97994   11114  1.0  19.07   1.0     2.0       0.0  0.243740  0.267923   
27  105979   10791  2.0  24.00   1.0     2.0       0.0  0.119005  0.399663   
28   92155   11022  NaN    NaN   NaN     NaN       NaN  0.125406  0.235575   
29   90877   10907  NaN    NaN   NaN     NaN       NaN  0.114077  0.270397   
30  112126   11157  2.0  21.83   2.0     2.0       0.0  0.102177  0.097644   
31   89095   11100  2.0  24.62   1.0     2.0       0.0  0.128059  0.364319   

    DorsAttn  ...  avgCEST_Vis  ctCEST_Vis  avgCEST_Limbic  ctCEST_Limbic  \
0   0.402285  ...    14.297329       175.0        8.867364          391.0   
1   0.257396  ...    10.610277       122.0        8.370049          258.0   
2   0.258377  ...     8.967306        46.0       10.076208           62.0   
3   0.287490  ...     9.610587       194.0        5.855585          267.0   
4   0.520766  ...     8.387602       214.0        6.432389          304.0   
5   0.316759  ...    12.741945       147.0       10.419856          189.0   
6   0.371144  ...     8.465859       293.0        3.779353          162.0   
7   0.063928  ...     9.343160       153.0        5.411261          236.0   
8   0.241768  ...          NaN         NaN             NaN            NaN   
9   0.292959  ...    13.178692       110.0        6.876791          116.0   
10  0.243422  ...     9.746861       184.0        7.552845          168.0   
11  0.429669  ...    10.589238       300.0        6.771436          247.0   
12  0.238394  ...          NaN         NaN             NaN            NaN   
13  0.278428  ...    12.802977       294.0        6.410949          320.0   
14  0.176538  ...    13.682704       292.0        5.676007          220.0   
15  0.330240  ...     9.176037       161.0        4.421310          533.0   
16  0.309288  ...     9.737379        21.0        5.850790          187.0   
17  0.510831  ...    10.190264       154.0        6.229536          279.0   
18  0.200455  ...     9.714172       242.0        6.640281          229.0   
19  0.335207  ...    10.088696       125.0        7.649896          287.0   
20  0.294042  ...    11.131604       152.0        7.082764          264.0   
21  0.479209  ...     9.479773        27.0        6.994899          147.0   
22  0.286440  ...          NaN         NaN             NaN            NaN   
23  0.218326  ...    13.034999       138.0        5.861445          411.0   
24  0.401307  ...    10.633214       128.0        7.509117          298.0   
25  0.219597  ...    12.498565       146.0        7.559701          369.0   
26  0.494674  ...     7.216132       190.0        4.831669          114.0   
27  0.631075  ...    12.373299       162.0        7.654502          258.0   
28  0.306728  ...          NaN         NaN             NaN            NaN   
29  0.251416  ...          NaN         NaN             NaN            NaN   
30  0.313409  ...    13.772174       114.0        8.371397           73.0   
31  0.473277  ...    12.511567       140.0        6.177131           95.0   

    avgCEST_SalVentAttn  ctCEST_SalVentAttn    tap_tot  er40_cr    medf_pc  \
0              8.034846              2143.0        NaN     39.0        NaN   
1              7.546548              1753.0        NaN      NaN        NaN   
2              7.277624              1245.0  104.00000     38.0  88.888889   
3              7.863291              1068.0  103.33330     38.0  77.777778   
4              8.805663              1815.0        NaN     39.0        NaN   
5              7.181154              1830.0   72.33333      NaN  91.666667   
6              8.565783              1634.0        NaN      NaN        NaN   
7              8.124917              1393.0   92.00000     34.0  69.444444   
8                   NaN                 NaN        NaN      NaN        NaN   
9              8.867149              1667.0        NaN      NaN        NaN   
10             7.937581              1527.0  105.33330     39.0  77.777778   
11             7.662420              1683.0   97.00000     39.0  66.666667   
12                  NaN                 NaN   95.00000     40.0  83.333333   
13             8.939010              1862.0  116.33330     37.0  69.444444   
14             7.325671              1855.0   92.66666      NaN  72.222222   
15             7.404367              1885.0   93.33333     37.0  80.555556   
16             7.365407              1667.0  117.00000     37.0  86.111111   
17             7.774126              1705.0  103.33330     36.0  86.111111   
18             8.103207              2062.0  128.00000     38.0  66.666667   
19             7.989862              1001.0  120.00000     38.0  91.666667   
20             8.076560              1678.0        NaN      NaN        NaN   
21             8.118948              1817.0        NaN      NaN        NaN   
22                  NaN                 NaN        NaN      NaN        NaN   
23             8.168039              1530.0        NaN      NaN        NaN   
24             7.920916              1806.0   96.33333     40.0  69.444444   
25             8.152667              1849.0  110.00000     38.0  88.888889   
26             7.594341              1556.0  101.66670     38.0  75.000000   
27             8.100647              1514.0   92.00000     40.0  80.555556   
28                  NaN                 NaN        NaN      NaN        NaN   
29                  NaN                 NaN        NaN      NaN        NaN   
30             8.358057              1331.0  104.66670     39.0  83.333333   
31             8.547472              1716.0  125.66670     36.0  86.111111   

    hstatus  
0        HC  
1       NaN  
2     Other  
3       PSY  
4     Other  
5        HC  
6       NaN  
7     Other  
8     Other  
9        HC  
10      PSY  
11       HC  
12       HC  
13      PSY  
14       HC  
15      PSY  
16       HC  
17       HC  
18      PSY  
19      PSY  
20      PSY  
21    Other  
22    Other  
23      NaN  
24      PSY  
25      PSY  
26       HC  
27    Other  
28    Other  
29    Other  
30       HC  
31    Other  

[32 rows x 32 columns]

  hstatus_  avgCEST_SalVentAttn_mean  avgCEST_SalVentAttn_std  \
0       HC                  7.795908                 0.543594   
1    Other                  8.162545                 0.520001   
2      PSY                  8.043051                 0.401342   

   SalVentAttn_mean  SalVentAttn_std  
0          0.368396         0.106964  
1          0.329335         0.081924  
2          0.366513         0.084872  

     BBLID Session  sex    age  race  ethnic  dateDiff      Cont   Default  \
0    19830   10789  2.0  21.92   1.0     2.0       0.0  0.175604  0.376170   
1    20645   11260  1.0  19.84   2.0     2.0       0.0       NaN  0.210973   
2   125511   10906  2.0  20.86   1.0     2.0       0.0       NaN  0.260692   
3   116019   11135  2.0  25.72   2.0     1.0       0.0  0.121458  0.322248   
4    19790   10819  2.0  23.37   1.0     2.0       0.0  0.248519  0.331945   
5    80557   10738  2.0  29.28   2.0     2.0       0.0       NaN       NaN   
6    20642   11261  1.0  25.59   NaN     NaN       0.0  0.232299  0.216163   
7    20180   11011  1.0  15.43   1.0     2.0      15.0  0.096672  0.101490   
8   139272   10739  NaN    NaN   NaN     NaN       NaN  0.085998  0.153989   
9   121085   10851  2.0  20.84   1.0     2.0      14.0  0.201904  0.159492   
10   93292   10938  2.0  25.32   2.0     2.0       0.0  0.246400  0.168437   
11   90281   10902  2.0  22.89   1.0     2.0      52.0       NaN  0.235045   
12   96659   11096  1.0  17.74   1.0     2.0       0.0  0.220468  0.291296   
13   93274   10765  2.0  24.64   5.0     2.0      11.0  0.222760  0.233656   
14  106880   10699  1.0  22.62   2.0     2.0       0.0  0.341071  0.069097   
15   90077   10962  2.0  21.34   1.0     2.0       0.0  0.165071  0.196767   
16  121407   10688  2.0  25.78   2.0     2.0       0.0  0.169243  0.402548   
17  102041   10821  1.0  23.43   1.0     2.0       0.0  0.261861  0.323492   
18  119791   10705  1.0  21.70   5.0     2.0       0.0  0.231845  0.195510   
19  106057   11122  2.0  20.12   1.0     2.0       5.0  0.201449  0.240199   
20   20011   10888  2.0  21.42   1.0     2.0       0.0  0.259585  0.281621   
21   20543   11259  1.0  21.51   1.0     1.0       0.0  0.218721  0.579795   
22   87225   10933  NaN    NaN   NaN     NaN       NaN       NaN  0.230372   
23   19981   11106  2.0  23.33   2.0     2.0       0.0  0.150234  0.321884   
24  132641   10692  2.0  23.73   2.0     2.0       0.0  0.233773       NaN   
25   88608   10764  1.0  25.36   2.0     2.0       1.0  0.201716  0.156970   
26   97994   11114  1.0  19.07   1.0     2.0       0.0  0.243740  0.267923   
27  105979   10791  2.0  24.00   1.0     2.0       0.0  0.119005  0.399663   
28   92155   11022  NaN    NaN   NaN     NaN       NaN  0.125406  0.235575   
29   90877   10907  NaN    NaN   NaN     NaN       NaN  0.114077  0.270397   
30  112126   11157  2.0  21.83   2.0     2.0       0.0  0.102177  0.097644   
31   89095   11100  2.0  24.62   1.0     2.0       0.0  0.128059  0.364319   

    DorsAttn  ...  avgCEST_Vis  ctCEST_Vis  avgCEST_Limbic  ctCEST_Limbic  \
0   0.402285  ...    14.297329       175.0        8.867364          391.0   
1   0.257396  ...    10.610277       122.0        8.370049          258.0   
2   0.258377  ...     8.967306        46.0       10.076208           62.0   
3   0.287490  ...     9.610587       194.0        5.855585          267.0   
4   0.520766  ...     8.387602       214.0        6.432389          304.0   
5   0.316759  ...    12.741945       147.0       10.419856          189.0   
6   0.371144  ...     8.465859       293.0        3.779353          162.0   
7   0.063928  ...     9.343160       153.0        5.411261          236.0   
8   0.241768  ...          NaN         NaN             NaN            NaN   
9   0.292959  ...    13.178692       110.0        6.876791          116.0   
10  0.243422  ...     9.746861       184.0        7.552845          168.0   
11  0.429669  ...    10.589238       300.0        6.771436          247.0   
12  0.238394  ...          NaN         NaN             NaN            NaN   
13  0.278428  ...    12.802977       294.0        6.410949          320.0   
14  0.176538  ...    13.682704       292.0        5.676007          220.0   
15  0.330240  ...     9.176037       161.0        4.421310          533.0   
16  0.309288  ...     9.737379        21.0        5.850790          187.0   
17  0.510831  ...    10.190264       154.0        6.229536          279.0   
18  0.200455  ...     9.714172       242.0        6.640281          229.0   
19  0.335207  ...    10.088696       125.0        7.649896          287.0   
20  0.294042  ...    11.131604       152.0        7.082764          264.0   
21  0.479209  ...     9.479773        27.0        6.994899          147.0   
22  0.286440  ...          NaN         NaN             NaN            NaN   
23  0.218326  ...    13.034999       138.0        5.861445          411.0   
24  0.401307  ...    10.633214       128.0        7.509117          298.0   
25  0.219597  ...    12.498565       146.0        7.559701          369.0   
26  0.494674  ...     7.216132       190.0        4.831669          114.0   
27  0.631075  ...    12.373299       162.0        7.654502          258.0   
28  0.306728  ...          NaN         NaN             NaN            NaN   
29  0.251416  ...          NaN         NaN             NaN            NaN   
30  0.313409  ...    13.772174       114.0        8.371397           73.0   
31  0.473277  ...    12.511567       140.0        6.177131           95.0   

    avgCEST_SalVentAttn  ctCEST_SalVentAttn    tap_tot  er40_cr    medf_pc  \
0              8.034846              2143.0        NaN     39.0        NaN   
1              7.546548              1753.0        NaN      NaN        NaN   
2              7.277624              1245.0  104.00000     38.0  88.888889   
3              7.863291              1068.0  103.33330     38.0  77.777778   
4              8.805663              1815.0        NaN     39.0        NaN   
5              7.181154              1830.0   72.33333      NaN  91.666667   
6              8.565783              1634.0        NaN      NaN        NaN   
7              8.124917              1393.0   92.00000     34.0  69.444444   
8                   NaN                 NaN        NaN      NaN        NaN   
9              8.867149              1667.0        NaN      NaN        NaN   
10             7.937581              1527.0  105.33330     39.0  77.777778   
11             7.662420              1683.0   97.00000     39.0  66.666667   
12                  NaN                 NaN   95.00000     40.0  83.333333   
13             8.939010              1862.0  116.33330     37.0  69.444444   
14             7.325671              1855.0   92.66666      NaN  72.222222   
15             7.404367              1885.0   93.33333     37.0  80.555556   
16             7.365407              1667.0  117.00000     37.0  86.111111   
17             7.774126              1705.0  103.33330     36.0  86.111111   
18             8.103207              2062.0  128.00000     38.0  66.666667   
19             7.989862              1001.0  120.00000     38.0  91.666667   
20             8.076560              1678.0        NaN      NaN        NaN   
21             8.118948              1817.0        NaN      NaN        NaN   
22                  NaN                 NaN        NaN      NaN        NaN   
23             8.168039              1530.0        NaN      NaN        NaN   
24             7.920916              1806.0   96.33333     40.0  69.444444   
25             8.152667              1849.0  110.00000     38.0  88.888889   
26             7.594341              1556.0  101.66670     38.0  75.000000   
27             8.100647              1514.0   92.00000     40.0  80.555556   
28                  NaN                 NaN        NaN      NaN        NaN   
29                  NaN                 NaN        NaN      NaN        NaN   
30             8.358057              1331.0  104.66670     39.0  83.333333   
31             8.547472              1716.0  125.66670     36.0  86.111111   

    hstatus  
0        HC  
1       NaN  
2     Other  
3       PSY  
4     Other  
5        HC  
6       NaN  
7     Other  
8     Other  
9        HC  
10      PSY  
11       HC  
12       HC  
13      PSY  
14       HC  
15      PSY  
16       HC  
17       HC  
18      PSY  
19      PSY  
20      PSY  
21    Other  
22    Other  
23      NaN  
24      PSY  
25      PSY  
26       HC  
27    Other  
28    Other  
29    Other  
30       HC  
31    Other  

[32 rows x 32 columns]

  hstatus_  avgCEST_SomMot_mean  avgCEST_SomMot_std  SomMot_mean  SomMot_std
0       HC             7.627695            0.678048     0.425217    0.098687
1    Other             8.095808            0.601198     0.413422    0.118454
2      PSY             7.970517            0.419776     0.458780    0.122978

     BBLID Session  sex    age  race  ethnic  dateDiff      Cont   Default  \
0    19830   10789  2.0  21.92   1.0     2.0       0.0  0.175604  0.376170   
1    20645   11260  1.0  19.84   2.0     2.0       0.0       NaN  0.210973   
2   125511   10906  2.0  20.86   1.0     2.0       0.0       NaN  0.260692   
3   116019   11135  2.0  25.72   2.0     1.0       0.0  0.121458  0.322248   
4    19790   10819  2.0  23.37   1.0     2.0       0.0  0.248519  0.331945   
5    80557   10738  2.0  29.28   2.0     2.0       0.0       NaN       NaN   
6    20642   11261  1.0  25.59   NaN     NaN       0.0  0.232299  0.216163   
7    20180   11011  1.0  15.43   1.0     2.0      15.0  0.096672  0.101490   
8   139272   10739  NaN    NaN   NaN     NaN       NaN  0.085998  0.153989   
9   121085   10851  2.0  20.84   1.0     2.0      14.0  0.201904  0.159492   
10   93292   10938  2.0  25.32   2.0     2.0       0.0  0.246400  0.168437   
11   90281   10902  2.0  22.89   1.0     2.0      52.0       NaN  0.235045   
12   96659   11096  1.0  17.74   1.0     2.0       0.0  0.220468  0.291296   
13   93274   10765  2.0  24.64   5.0     2.0      11.0  0.222760  0.233656   
14  106880   10699  1.0  22.62   2.0     2.0       0.0  0.341071  0.069097   
15   90077   10962  2.0  21.34   1.0     2.0       0.0  0.165071  0.196767   
16  121407   10688  2.0  25.78   2.0     2.0       0.0  0.169243  0.402548   
17  102041   10821  1.0  23.43   1.0     2.0       0.0  0.261861  0.323492   
18  119791   10705  1.0  21.70   5.0     2.0       0.0  0.231845  0.195510   
19  106057   11122  2.0  20.12   1.0     2.0       5.0  0.201449  0.240199   
20   20011   10888  2.0  21.42   1.0     2.0       0.0  0.259585  0.281621   
21   20543   11259  1.0  21.51   1.0     1.0       0.0  0.218721  0.579795   
22   87225   10933  NaN    NaN   NaN     NaN       NaN       NaN  0.230372   
23   19981   11106  2.0  23.33   2.0     2.0       0.0  0.150234  0.321884   
24  132641   10692  2.0  23.73   2.0     2.0       0.0  0.233773       NaN   
25   88608   10764  1.0  25.36   2.0     2.0       1.0  0.201716  0.156970   
26   97994   11114  1.0  19.07   1.0     2.0       0.0  0.243740  0.267923   
27  105979   10791  2.0  24.00   1.0     2.0       0.0  0.119005  0.399663   
28   92155   11022  NaN    NaN   NaN     NaN       NaN  0.125406  0.235575   
29   90877   10907  NaN    NaN   NaN     NaN       NaN  0.114077  0.270397   
30  112126   11157  2.0  21.83   2.0     2.0       0.0  0.102177  0.097644   
31   89095   11100  2.0  24.62   1.0     2.0       0.0  0.128059  0.364319   

    DorsAttn  ...  avgCEST_Vis  ctCEST_Vis  avgCEST_Limbic  ctCEST_Limbic  \
0   0.402285  ...    14.297329       175.0        8.867364          391.0   
1   0.257396  ...    10.610277       122.0        8.370049          258.0   
2   0.258377  ...     8.967306        46.0       10.076208           62.0   
3   0.287490  ...     9.610587       194.0        5.855585          267.0   
4   0.520766  ...     8.387602       214.0        6.432389          304.0   
5   0.316759  ...    12.741945       147.0       10.419856          189.0   
6   0.371144  ...     8.465859       293.0        3.779353          162.0   
7   0.063928  ...     9.343160       153.0        5.411261          236.0   
8   0.241768  ...          NaN         NaN             NaN            NaN   
9   0.292959  ...    13.178692       110.0        6.876791          116.0   
10  0.243422  ...     9.746861       184.0        7.552845          168.0   
11  0.429669  ...    10.589238       300.0        6.771436          247.0   
12  0.238394  ...          NaN         NaN             NaN            NaN   
13  0.278428  ...    12.802977       294.0        6.410949          320.0   
14  0.176538  ...    13.682704       292.0        5.676007          220.0   
15  0.330240  ...     9.176037       161.0        4.421310          533.0   
16  0.309288  ...     9.737379        21.0        5.850790          187.0   
17  0.510831  ...    10.190264       154.0        6.229536          279.0   
18  0.200455  ...     9.714172       242.0        6.640281          229.0   
19  0.335207  ...    10.088696       125.0        7.649896          287.0   
20  0.294042  ...    11.131604       152.0        7.082764          264.0   
21  0.479209  ...     9.479773        27.0        6.994899          147.0   
22  0.286440  ...          NaN         NaN             NaN            NaN   
23  0.218326  ...    13.034999       138.0        5.861445          411.0   
24  0.401307  ...    10.633214       128.0        7.509117          298.0   
25  0.219597  ...    12.498565       146.0        7.559701          369.0   
26  0.494674  ...     7.216132       190.0        4.831669          114.0   
27  0.631075  ...    12.373299       162.0        7.654502          258.0   
28  0.306728  ...          NaN         NaN             NaN            NaN   
29  0.251416  ...          NaN         NaN             NaN            NaN   
30  0.313409  ...    13.772174       114.0        8.371397           73.0   
31  0.473277  ...    12.511567       140.0        6.177131           95.0   

    avgCEST_SalVentAttn  ctCEST_SalVentAttn    tap_tot  er40_cr    medf_pc  \
0              8.034846              2143.0        NaN     39.0        NaN   
1              7.546548              1753.0        NaN      NaN        NaN   
2              7.277624              1245.0  104.00000     38.0  88.888889   
3              7.863291              1068.0  103.33330     38.0  77.777778   
4              8.805663              1815.0        NaN     39.0        NaN   
5              7.181154              1830.0   72.33333      NaN  91.666667   
6              8.565783              1634.0        NaN      NaN        NaN   
7              8.124917              1393.0   92.00000     34.0  69.444444   
8                   NaN                 NaN        NaN      NaN        NaN   
9              8.867149              1667.0        NaN      NaN        NaN   
10             7.937581              1527.0  105.33330     39.0  77.777778   
11             7.662420              1683.0   97.00000     39.0  66.666667   
12                  NaN                 NaN   95.00000     40.0  83.333333   
13             8.939010              1862.0  116.33330     37.0  69.444444   
14             7.325671              1855.0   92.66666      NaN  72.222222   
15             7.404367              1885.0   93.33333     37.0  80.555556   
16             7.365407              1667.0  117.00000     37.0  86.111111   
17             7.774126              1705.0  103.33330     36.0  86.111111   
18             8.103207              2062.0  128.00000     38.0  66.666667   
19             7.989862              1001.0  120.00000     38.0  91.666667   
20             8.076560              1678.0        NaN      NaN        NaN   
21             8.118948              1817.0        NaN      NaN        NaN   
22                  NaN                 NaN        NaN      NaN        NaN   
23             8.168039              1530.0        NaN      NaN        NaN   
24             7.920916              1806.0   96.33333     40.0  69.444444   
25             8.152667              1849.0  110.00000     38.0  88.888889   
26             7.594341              1556.0  101.66670     38.0  75.000000   
27             8.100647              1514.0   92.00000     40.0  80.555556   
28                  NaN                 NaN        NaN      NaN        NaN   
29                  NaN                 NaN        NaN      NaN        NaN   
30             8.358057              1331.0  104.66670     39.0  83.333333   
31             8.547472              1716.0  125.66670     36.0  86.111111   

    hstatus  
0        HC  
1       NaN  
2     Other  
3       PSY  
4     Other  
5        HC  
6       NaN  
7     Other  
8     Other  
9        HC  
10      PSY  
11       HC  
12       HC  
13      PSY  
14       HC  
15      PSY  
16       HC  
17       HC  
18      PSY  
19      PSY  
20      PSY  
21    Other  
22    Other  
23      NaN  
24      PSY  
25      PSY  
26       HC  
27    Other  
28    Other  
29    Other  
30       HC  
31    Other  

[32 rows x 32 columns]

  hstatus_  avgCEST_Limbic_mean  avgCEST_Limbic_std  Limbic_mean  Limbic_std
0       HC             7.099427            1.782223     0.347572    0.028164
1    Other             7.124398            1.632299     0.458920    0.178272
2      PSY             6.742494            1.069243     0.475692    0.100969
In [99]:
 

hstatus
NC         23
PRO        17
Unknown     6
S           6
PSY         4
O           2
PROR        2
MDD         2
Name: count, dtype: int64
hstatus
PSY      29
HC       23
Other    10
Name: count, dtype: int64
Intercept: 98.15734353062948
Coefficients: avgCEST_Cont     2.697674
Cont           -59.573144
dtype: float64
Cont-tap_tot ANOVA Results:
                   sum_sq    df         F    PR(>F)
avgCEST_Cont    65.282549   1.0  0.470055  0.497478
Cont           620.575015   1.0  4.468335  0.041727
Residual      4860.899631  35.0       NaN       NaN
Intercept: 29.333477223416942
Coefficients: avgCEST_Cont    1.077393
Cont           -3.241797
dtype: float64
Cont-er40_cr ANOVA Results:
                  sum_sq    df         F   PR(>F)
avgCEST_Cont    9.940285   1.0  2.200472  0.14718
Cont            1.800830   1.0  0.398648  0.53201
Residual      153.589643  34.0       NaN      NaN
Intercept: 115.79929901723546
Coefficients: avgCEST_Cont   -4.534770
Cont            7.701735
dtype: float64
Cont-medf_pc ANOVA Results:
                   sum_sq    df         F    PR(>F)
avgCEST_Cont   184.950421   1.0  2.530715  0.120394
Cont            10.394443   1.0  0.142229  0.708289
Residual      2630.962333  36.0       NaN       NaN
Intercept: 122.74213574519109
Coefficients: avgCEST_Default   -1.758623
Default           -7.896795
dtype: float64
Default-tap_tot ANOVA Results:
                      sum_sq    df         F    PR(>F)
avgCEST_Default    35.643597   1.0  0.229076  0.635186
Default            14.469524   1.0  0.092994  0.762212
Residual         5445.899572  35.0       NaN       NaN
Intercept: 32.20208139781507
Coefficients: avgCEST_Default    0.622225
Default            1.329105
dtype: float64
Default-er40_cr ANOVA Results:
                     sum_sq    df         F    PR(>F)
avgCEST_Default    4.853881   1.0  1.028229  0.317534
Default            0.442673   1.0  0.093774  0.761247
Residual         165.221754  35.0       NaN       NaN
Intercept: 100.17272823532824
Coefficients: avgCEST_Default   -2.232662
Default           -6.430613
dtype: float64
Default-medf_pc ANOVA Results:
                      sum_sq    df         F    PR(>F)
avgCEST_Default    64.185954   1.0  0.867039  0.357813
Default            10.478025   1.0  0.141540  0.708904
Residual         2739.069952  37.0       NaN       NaN
Intercept: 98.48317557492753
Coefficients: avgCEST_DorsAttn     1.608352
DorsAttn           -11.853633
dtype: float64
DorsAttn-tap_tot ANOVA Results:
                       sum_sq    df         F    PR(>F)
avgCEST_DorsAttn   127.291965   1.0  0.822087  0.370952
DorsAttn            57.925357   1.0  0.374098  0.544847
Residual          5264.561248  34.0       NaN       NaN
Intercept: 34.49962067296072
Coefficients: avgCEST_DorsAttn    0.343066
DorsAttn            0.388906
dtype: float64
DorsAttn-er40_cr ANOVA Results:
                      sum_sq    df         F    PR(>F)
avgCEST_DorsAttn    5.404832   1.0  1.111523  0.299186
DorsAttn            0.055687   1.0  0.011452  0.915406
Residual          165.326548  34.0       NaN       NaN
Intercept: 76.58981050313344
Coefficients: avgCEST_DorsAttn    -0.426495
DorsAttn            20.814416
dtype: float64
DorsAttn-medf_pc ANOVA Results:
                       sum_sq    df         F    PR(>F)
avgCEST_DorsAttn     8.972726   1.0  0.124570  0.726188
DorsAttn           181.624474   1.0  2.521525  0.121047
Residual          2593.065856  36.0       NaN       NaN
Intercept: 121.05404292285397
Coefficients: avgCEST_Vis   -1.345877
Vis            0.567614
dtype: float64
Vis-tap_tot ANOVA Results:
                  sum_sq    df         F    PR(>F)
avgCEST_Vis   558.293498   1.0  4.004387  0.053184
Vis             0.112394   1.0  0.000806  0.977510
Residual     4879.716656  35.0       NaN       NaN
Intercept: 31.156963939739214
Coefficients: avgCEST_Vis    0.297392
Vis            6.045508
dtype: float64
Vis-er40_cr ANOVA Results:
                 sum_sq    df         F    PR(>F)
avgCEST_Vis   28.298582   1.0  7.293807  0.010590
Vis           14.206267   1.0  3.661589  0.063888
Residual     135.793333  35.0       NaN       NaN
Intercept: 75.38431425667916
Coefficients: avgCEST_Vis    -0.396246
Vis            19.487511
dtype: float64
Vis-medf_pc ANOVA Results:
                  sum_sq    df         F    PR(>F)
avgCEST_Vis    51.852995   1.0  0.747893  0.392717
Vis           148.350756   1.0  2.139711  0.151972
Residual     2565.288933  37.0       NaN       NaN
Intercept: 88.13929846429112
Coefficients: avgCEST_SalVentAttn     4.252572
SalVentAttn           -36.026225
dtype: float64
SalVentAttn-tap_tot ANOVA Results:
                          sum_sq    df         F    PR(>F)
avgCEST_SalVentAttn   318.842198   1.0  2.348598  0.134386
SalVentAttn           331.952370   1.0  2.445168  0.126885
Residual             4751.548507  35.0       NaN       NaN
Intercept: 35.30826117361063
Coefficients: avgCEST_SalVentAttn    0.139538
SalVentAttn            2.490422
dtype: float64
SalVentAttn-er40_cr ANOVA Results:
                         sum_sq    df         F    PR(>F)
avgCEST_SalVentAttn    0.341213   1.0  0.070517  0.792144
SalVentAttn            1.491608   1.0  0.308263  0.582280
Residual             169.356536  35.0       NaN       NaN
Intercept: 79.11838145735956
Coefficients: avgCEST_SalVentAttn    -0.400522
SalVentAttn            12.462481
dtype: float64
SalVentAttn-medf_pc ANOVA Results:
                          sum_sq    df         F    PR(>F)
avgCEST_SalVentAttn     2.880867   1.0  0.038398  0.845717
SalVentAttn            39.893415   1.0  0.531731  0.470471
Residual             2775.946235  37.0       NaN       NaN
Intercept: 91.70496871655598
Coefficients: avgCEST_SomMot     5.991925
SomMot           -58.524134
dtype: float64
SomMot-tap_tot ANOVA Results:
                     sum_sq    df         F    PR(>F)
avgCEST_SomMot   835.774243   1.0  7.040765  0.011901
SomMot           927.078511   1.0  7.809934  0.008375
Residual        4154.676386  35.0       NaN       NaN
Intercept: 39.05182884112296
Coefficients: avgCEST_SomMot   -0.092637
SomMot           -1.815914
dtype: float64
SomMot-er40_cr ANOVA Results:
                    sum_sq    df         F    PR(>F)
avgCEST_SomMot    0.202848   1.0  0.041878  0.839038
SomMot            0.940487   1.0  0.194162  0.662184
Residual        169.533512  35.0       NaN       NaN
Intercept: 76.20130955735688
Coefficients: avgCEST_SomMot    -0.294569
SomMot            13.412113
dtype: float64
SomMot-medf_pc ANOVA Results:
                     sum_sq    df         F    PR(>F)
avgCEST_SomMot     2.153254   1.0  0.028807  0.866150
SomMot            56.371411   1.0  0.754156  0.390762
Residual        2765.663941  37.0       NaN       NaN
Intercept: 106.10749624874113
Coefficients: avgCEST_Limbic    -2.511216
Limbic            33.444777
dtype: float64
Limbic-tap_tot ANOVA Results:
                     sum_sq    df         F    PR(>F)
avgCEST_Limbic   342.287811   1.0  1.840251  0.191693
Limbic           329.034270   1.0  1.768995  0.200115
Residual        3348.011802  18.0       NaN       NaN

/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])

Intercept: 33.82998607543426
Coefficients: avgCEST_Limbic    0.551864
Limbic            0.235921
dtype: float64
Limbic-er40_cr ANOVA Results:
                   sum_sq    df         F    PR(>F)
avgCEST_Limbic  11.949987   1.0  3.055763  0.099610
Limbic           0.015180   1.0  0.003882  0.951094
Residual        62.570223  16.0       NaN       NaN
Intercept: 97.47546461686645
Coefficients: avgCEST_Limbic    -0.436728
Limbic           -31.264380
dtype: float64
Limbic-medf_pc ANOVA Results:
                     sum_sq    df         F    PR(>F)
avgCEST_Limbic    10.352499   1.0  0.137690  0.714920
Limbic           287.530662   1.0  3.824205  0.066223
Residual        1353.366817  18.0       NaN       NaN

/var/folders/ls/hy_z7hgd4_13km3h7j84vqh40000gp/T/ipykernel_3148/2516526184.py:34: FutureWarning: Series.__getitem__ treating keys as positions is deprecated. In a future version, integer keys will always be treated as labels (consistent with DataFrame behavior). To access a value by position, use `ser.iloc[pos]`
  print('Intercept:', model.params[0])
In [127]:
#CLUNKIER COMPREHENSIVE VERSION:

from sklearn import linear_model
import statsmodels.api as sm
import statsmodels.formula.api as smf

if run_grpanalysis:
    # Curate data 
    value_counts = grp_df['hstatus'].value_counts()
    print(value_counts)
    grp_df['hstatus'] = grp_df['hstatus'].replace('NC', 'HC')
    grp_df['hstatus'] = grp_df['hstatus'].replace('PROR', 'PSY')
    grp_df['hstatus'] = grp_df['hstatus'].replace('PRO', 'PSY')
    grp_df['hstatus'] = grp_df['hstatus'].replace('S', 'PSY')
    grp_df['hstatus'] = grp_df['hstatus'].replace('O', 'Other')
    grp_df['hstatus'] = grp_df['hstatus'].replace('Unknown', 'Other')
    grp_df['hstatus'] = grp_df['hstatus'].replace('MDD', 'Other')
    value_counts = grp_df['hstatus'].value_counts()
    print(value_counts)
    
    colors = pd.DataFrame({'Network': ["Cont", "Default", "DorsAttn", "Vis", "SalVentAttn", "SomMot", "Limbic"],
        'Color': ['PuOr', 'PuRd_r', 'PiYG_r', 'PRGn', 'PiYG', 'GnBu_r', 'terrain_r']}) # 
    
    anova_tables = []
    # Create a scatter plot with a multiple linear regression 
    for network in networks:
        cestcol = "avgCEST_" + network
        # Create a linear regression model for fcon
       # color = colors.loc[colors['Network'] == network, 'Color'].values[0]
       # sns.set_palette(color)

        # Create CNB correlation plot for each network fcon and cest 
        for CNB_score in CNB_scores:
         #   graph_df = grp_df[grp_df['hstatus'] != 'Other']
            graph_df = grp_df
            graph_df = graph_df.dropna(subset=[CNB_score, cestcol, network])
            graph_df = graph_df[[CNB_score, cestcol, network]]
            # Define x values and target variable
            X = graph_df[[cestcol, network]]
            Y = graph_df[CNB_score]

            ##################################
            # Define formula and model
            formula = f'{CNB_score} ~ {cestcol} + {network}'
            model = smf.ols(formula=formula, data=graph_df).fit()
            print('\n\n\n' + network + ' & ' + CNB_score) 
            print(model.summary())

            # Plotting the regression line
            fig, ax = plt.subplots()
            ax.scatter(graph_df[cestcol], graph_df[CNB_score], label='Actual Data')
            # Generate x values for the line
            x_line = pd.DataFrame({cestcol: np.linspace(graph_df[cestcol].min(), graph_df[cestcol].max(), 100),
                                   network: np.mean(graph_df[network])})  # Use mean value for the network variable
            # Predictions for the regression line
            y_line = model.predict(x_line)
            # Plot the regression line
            ax.plot(x_line[cestcol], y_line, color='red', label='Regression Line')
            ax.set_xlabel(cestcol)
            ax.set_ylabel(CNB_score)
            ax.set_title('Multiple Linear Regression Plot for ' + CNB_score + ' & ' + network)
            ax.legend()
            plt.show()

hstatus
NC         10
PRO         7
Unknown     4
O           3
MDD         3
PSY         2
Name: count, dtype: int64
hstatus
HC       10
Other    10
PSY       9
Name: count, dtype: int64



Cont & tap_tot
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                tap_tot   R-squared:                       0.024
Model:                            OLS   Adj. R-squared:                 -0.126
Method:                 Least Squares   F-statistic:                    0.1631
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.851
Time:                        19:10:01   Log-Likelihood:                -61.774
No. Observations:                  16   AIC:                             129.5
Df Residuals:                      13   BIC:                             131.9
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
================================================================================
                   coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------
Intercept       67.5854     68.496      0.987      0.342     -80.391     215.562
avgCEST_Cont     4.5797      8.019      0.571      0.578     -12.744      21.904
Cont             4.4999     48.771      0.092      0.928    -100.863     109.863
==============================================================================
Omnibus:                        2.237   Durbin-Watson:                   1.733
Prob(Omnibus):                  0.327   Jarque-Bera (JB):                1.628
Skew:                           0.614   Prob(JB):                        0.443
Kurtosis:                       2.034   Cond. No.                         187.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



Cont & er40_cr
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                er40_cr   R-squared:                       0.058
Model:                            OLS   Adj. R-squared:                 -0.076
Method:                 Least Squares   F-statistic:                    0.4322
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.657
Time:                        19:10:02   Log-Likelihood:                -30.555
No. Observations:                  17   AIC:                             67.11
Df Residuals:                      14   BIC:                             69.61
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
================================================================================
                   coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------
Intercept       33.6264      9.145      3.677      0.002      14.013      53.240
avgCEST_Cont     0.3706      1.101      0.337      0.741      -1.991       2.732
Cont             5.9738      7.173      0.833      0.419      -9.412      21.359
==============================================================================
Omnibus:                        0.570   Durbin-Watson:                   1.979
Prob(Omnibus):                  0.752   Jarque-Bera (JB):                0.322
Skew:                          -0.315   Prob(JB):                        0.851
Kurtosis:                       2.763   Cond. No.                         198.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



Cont & medf_pc
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                medf_pc   R-squared:                       0.140
Model:                            OLS   Adj. R-squared:                  0.008
Method:                 Least Squares   F-statistic:                     1.059
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.375
Time:                        19:10:02   Log-Likelihood:                -53.945
No. Observations:                  16   AIC:                             113.9
Df Residuals:                      13   BIC:                             116.2
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
================================================================================
                   coef    std err          t      P>|t|      [0.025      0.975]
--------------------------------------------------------------------------------
Intercept      130.7061     41.990      3.113      0.008      39.992     221.420
avgCEST_Cont    -5.5129      4.916     -1.121      0.282     -16.133       5.107
Cont           -32.4703     29.898     -1.086      0.297     -97.061      32.120
==============================================================================
Omnibus:                        0.229   Durbin-Watson:                   2.831
Prob(Omnibus):                  0.892   Jarque-Bera (JB):                0.406
Skew:                          -0.178   Prob(JB):                        0.816
Kurtosis:                       2.306   Cond. No.                         187.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=16
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



Default & tap_tot
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                tap_tot   R-squared:                       0.135
Model:                            OLS   Adj. R-squared:                  0.011
Method:                 Least Squares   F-statistic:                     1.088
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.364
Time:                        19:10:02   Log-Likelihood:                -64.073
No. Observations:                  17   AIC:                             134.1
Df Residuals:                      14   BIC:                             136.6
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
Intercept         156.9385     57.839      2.713      0.017      32.887     280.990
avgCEST_Default    -7.5511      7.482     -1.009      0.330     -23.597       8.495
Default            34.8139     28.622      1.216      0.244     -26.575      96.203
==============================================================================
Omnibus:                        0.974   Durbin-Watson:                   1.809
Prob(Omnibus):                  0.615   Jarque-Bera (JB):                0.901
Skew:                           0.417   Prob(JB):                        0.637
Kurtosis:                       2.241   Cond. No.                         164.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



Default & er40_cr

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "

                            OLS Regression Results                            
==============================================================================
Dep. Variable:                er40_cr   R-squared:                       0.038
Model:                            OLS   Adj. R-squared:                 -0.090
Method:                 Least Squares   F-statistic:                    0.2968
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.747
Time:                        19:10:02   Log-Likelihood:                -31.205
No. Observations:                  18   AIC:                             68.41
Df Residuals:                      15   BIC:                             71.08
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
Intercept          35.1400      7.562      4.647      0.000      19.023      51.257
avgCEST_Default     0.2557      0.995      0.257      0.801      -1.865       2.377
Default             2.5313      3.869      0.654      0.523      -5.715      10.777
==============================================================================
Omnibus:                        3.300   Durbin-Watson:                   2.191
Prob(Omnibus):                  0.192   Jarque-Bera (JB):                2.083
Skew:                          -0.833   Prob(JB):                        0.353
Kurtosis:                       2.992   Cond. No.                         169.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.



Default & medf_pc
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                medf_pc   R-squared:                       0.271
Model:                            OLS   Adj. R-squared:                  0.167
Method:                 Least Squares   F-statistic:                     2.604
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.109
Time:                        19:10:03   Log-Likelihood:                -56.738
No. Observations:                  17   AIC:                             119.5
Df Residuals:                      14   BIC:                             122.0
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
===================================================================================
                      coef    std err          t      P>|t|      [0.025      0.975]
-----------------------------------------------------------------------------------
Intercept         136.5065     37.569      3.633      0.003      55.928     217.085
avgCEST_Default    -8.3237      4.860     -1.713      0.109     -18.747       2.099
Default            32.5262     18.592      1.749      0.102      -7.349      72.402
==============================================================================
Omnibus:                        0.801   Durbin-Watson:                   1.960
Prob(Omnibus):                  0.670   Jarque-Bera (JB):                0.477
Skew:                          -0.392   Prob(JB):                        0.788
Kurtosis:                       2.759   Cond. No.                         164.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=17
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



DorsAttn & tap_tot
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                tap_tot   R-squared:                       0.116
Model:                            OLS   Adj. R-squared:                  0.005
Method:                 Least Squares   F-statistic:                     1.049
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.373
Time:                        19:10:03   Log-Likelihood:                -74.806
No. Observations:                  19   AIC:                             155.6
Df Residuals:                      16   BIC:                             158.4
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
====================================================================================
                       coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------------
Intercept           72.3800     24.623      2.940      0.010      20.181     124.579
avgCEST_DorsAttn     4.3225      3.015      1.434      0.171      -2.070      10.715
DorsAttn            -6.7606     23.666     -0.286      0.779     -56.931      43.410
==============================================================================
Omnibus:                        0.159   Durbin-Watson:                   1.634
Prob(Omnibus):                  0.924   Jarque-Bera (JB):                0.086
Skew:                          -0.112   Prob(JB):                        0.958
Kurtosis:                       2.758   Cond. No.                         69.9
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



DorsAttn & er40_cr
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                er40_cr   R-squared:                       0.187
Model:                            OLS   Adj. R-squared:                  0.086
Method:                 Least Squares   F-statistic:                     1.842
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.191
Time:                        19:10:03   Log-Likelihood:                -32.014
No. Observations:                  19   AIC:                             70.03
Df Residuals:                      16   BIC:                             72.86
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
====================================================================================
                       coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------------
Intercept           37.3622      2.638     14.165      0.000      31.771      42.954
avgCEST_DorsAttn    -0.1398      0.318     -0.440      0.666      -0.814       0.534
DorsAttn             4.6626      2.461      1.894      0.076      -0.555       9.880
==============================================================================
Omnibus:                        2.403   Durbin-Watson:                   2.016
Prob(Omnibus):                  0.301   Jarque-Bera (JB):                1.867
Skew:                          -0.736   Prob(JB):                        0.393
Kurtosis:                       2.563   Cond. No.                         71.5
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



DorsAttn & medf_pc
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                medf_pc   R-squared:                       0.150
Model:                            OLS   Adj. R-squared:                  0.043
Method:                 Least Squares   F-statistic:                     1.407
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.274
Time:                        19:10:03   Log-Likelihood:                -65.796
No. Observations:                  19   AIC:                             137.6
Df Residuals:                      16   BIC:                             140.4
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
====================================================================================
                       coef    std err          t      P>|t|      [0.025      0.975]
------------------------------------------------------------------------------------
Intercept           96.5777     15.325      6.302      0.000      64.090     129.066
avgCEST_DorsAttn    -2.7537      1.877     -1.467      0.162      -6.732       1.225
DorsAttn            13.1453     14.730      0.892      0.385     -18.080      44.371
==============================================================================
Omnibus:                        1.763   Durbin-Watson:                   2.939
Prob(Omnibus):                  0.414   Jarque-Bera (JB):                0.993
Skew:                           0.136   Prob(JB):                        0.609
Kurtosis:                       1.913   Cond. No.                         69.9
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



Vis & tap_tot
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                tap_tot   R-squared:                       0.068
Model:                            OLS   Adj. R-squared:                 -0.056
Method:                 Least Squares   F-statistic:                    0.5497
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.588
Time:                        19:10:04   Log-Likelihood:                -71.408
No. Observations:                  18   AIC:                             148.8
Df Residuals:                      15   BIC:                             151.5
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
===============================================================================
                  coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------
Intercept     122.9184     21.652      5.677      0.000      76.767     169.069
avgCEST_Vis    -1.0367      1.845     -0.562      0.582      -4.969       2.896
Vis           -15.1813     17.562     -0.864      0.401     -52.613      22.250
==============================================================================
Omnibus:                        2.237   Durbin-Watson:                   1.989
Prob(Omnibus):                  0.327   Jarque-Bera (JB):                0.771
Skew:                          -0.431   Prob(JB):                        0.680
Kurtosis:                       3.534   Cond. No.                         78.3
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



Vis & er40_cr
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                er40_cr   R-squared:                       0.048
Model:                            OLS   Adj. R-squared:                 -0.079
Method:                 Least Squares   F-statistic:                    0.3743
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.694
Time:                        19:10:04   Log-Likelihood:                -27.608
No. Observations:                  18   AIC:                             61.22
Df Residuals:                      15   BIC:                             63.89
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
===============================================================================
                  coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------
Intercept      36.5451      2.017     18.118      0.000      32.246      40.844
avgCEST_Vis     0.0818      0.155      0.527      0.606      -0.249       0.413
Vis             1.5206      2.042      0.745      0.468      -2.832       5.873
==============================================================================
Omnibus:                        0.284   Durbin-Watson:                   1.466
Prob(Omnibus):                  0.868   Jarque-Bera (JB):                0.436
Skew:                          -0.216   Prob(JB):                        0.804
Kurtosis:                       2.371   Cond. No.                         95.5
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



Vis & medf_pc
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                medf_pc   R-squared:                       0.083
Model:                            OLS   Adj. R-squared:                 -0.039
Method:                 Least Squares   F-statistic:                    0.6777
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.523
Time:                        19:10:04   Log-Likelihood:                -62.765
No. Observations:                  18   AIC:                             131.5
Df Residuals:                      15   BIC:                             134.2
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
===============================================================================
                  coef    std err          t      P>|t|      [0.025      0.975]
-------------------------------------------------------------------------------
Intercept      80.9322     13.396      6.041      0.000      52.379     109.485
avgCEST_Vis     0.4233      1.141      0.371      0.716      -2.010       2.856
Vis           -12.1276     10.865     -1.116      0.282     -35.286      11.031
==============================================================================
Omnibus:                        1.339   Durbin-Watson:                   2.564
Prob(Omnibus):                  0.512   Jarque-Bera (JB):                1.083
Skew:                          -0.401   Prob(JB):                        0.582
Kurtosis:                       2.105   Cond. No.                         78.3
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



SalVentAttn & tap_tot
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                tap_tot   R-squared:                       0.383
Model:                            OLS   Adj. R-squared:                  0.306
Method:                 Least Squares   F-statistic:                     4.960
Date:                Tue, 16 Jan 2024   Prob (F-statistic):             0.0211
Time:                        19:10:04   Log-Likelihood:                -71.393
No. Observations:                  19   AIC:                             148.8
Df Residuals:                      16   BIC:                             151.6
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=======================================================================================
                          coef    std err          t      P>|t|      [0.025      0.975]
---------------------------------------------------------------------------------------
Intercept             -62.5973     52.941     -1.182      0.254    -174.828      49.633
avgCEST_SalVentAttn    19.1111      6.165      3.100      0.007       6.042      32.180
SalVentAttn            44.1274     27.338      1.614      0.126     -13.826     102.081
==============================================================================
Omnibus:                        0.557   Durbin-Watson:                   2.088
Prob(Omnibus):                  0.757   Jarque-Bera (JB):                0.586
Skew:                           0.017   Prob(JB):                        0.746
Kurtosis:                       2.140   Cond. No.                         170.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



SalVentAttn & er40_cr
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                er40_cr   R-squared:                       0.031
Model:                            OLS   Adj. R-squared:                 -0.090
Method:                 Least Squares   F-statistic:                    0.2542
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.779
Time:                        19:10:05   Log-Likelihood:                -33.685
No. Observations:                  19   AIC:                             73.37
Df Residuals:                      16   BIC:                             76.20
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=======================================================================================
                          coef    std err          t      P>|t|      [0.025      0.975]
---------------------------------------------------------------------------------------
Intercept              34.5961      8.006      4.321      0.001      17.624      51.569
avgCEST_SalVentAttn     0.2868      0.909      0.316      0.756      -1.639       2.213
SalVentAttn             2.8508      3.998      0.713      0.486      -5.626      11.327
==============================================================================
Omnibus:                        2.990   Durbin-Watson:                   1.957
Prob(Omnibus):                  0.224   Jarque-Bera (JB):                2.020
Skew:                          -0.796   Prob(JB):                        0.364
Kurtosis:                       2.876   Cond. No.                         191.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



SalVentAttn & medf_pc
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                medf_pc   R-squared:                       0.083
Model:                            OLS   Adj. R-squared:                 -0.032
Method:                 Least Squares   F-statistic:                    0.7200
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.502
Time:                        19:10:05   Log-Likelihood:                -66.516
No. Observations:                  19   AIC:                             139.0
Df Residuals:                      16   BIC:                             141.9
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
=======================================================================================
                          coef    std err          t      P>|t|      [0.025      0.975]
---------------------------------------------------------------------------------------
Intercept             128.3183     40.957      3.133      0.006      41.494     215.142
avgCEST_SalVentAttn    -5.5351      4.769     -1.161      0.263     -15.645       4.575
SalVentAttn           -14.6959     21.149     -0.695      0.497     -59.530      30.138
==============================================================================
Omnibus:                        4.628   Durbin-Watson:                   2.466
Prob(Omnibus):                  0.099   Jarque-Bera (JB):                1.461
Skew:                          -0.019   Prob(JB):                        0.482
Kurtosis:                       1.642   Cond. No.                         170.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=19
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



SomMot & tap_tot
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                tap_tot   R-squared:                       0.265
Model:                            OLS   Adj. R-squared:                  0.167
Method:                 Least Squares   F-statistic:                     2.703
Date:                Tue, 16 Jan 2024   Prob (F-statistic):             0.0994
Time:                        19:10:05   Log-Likelihood:                -69.694
No. Observations:                  18   AIC:                             145.4
Df Residuals:                      15   BIC:                             148.1
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
Intercept         -5.2784     47.966     -0.110      0.914    -107.515      96.958
avgCEST_SomMot    13.0905      5.633      2.324      0.035       1.084      25.097
SomMot            17.6375     26.561      0.664      0.517     -38.975      74.250
==============================================================================
Omnibus:                        0.790   Durbin-Watson:                   1.925
Prob(Omnibus):                  0.674   Jarque-Bera (JB):                0.433
Skew:                           0.369   Prob(JB):                        0.805
Kurtosis:                       2.816   Cond. No.                         132.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



SomMot & er40_cr
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                er40_cr   R-squared:                       0.210
Model:                            OLS   Adj. R-squared:                  0.104
Method:                 Least Squares   F-statistic:                     1.989
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.171
Time:                        19:10:05   Log-Likelihood:                -30.560
No. Observations:                  18   AIC:                             67.12
Df Residuals:                      15   BIC:                             69.79
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
Intercept         40.0298      5.453      7.340      0.000      28.406      51.653
avgCEST_SomMot    -0.5365      0.643     -0.834      0.417      -1.908       0.835
SomMot             4.9555      2.981      1.662      0.117      -1.398      11.309
==============================================================================
Omnibus:                        0.156   Durbin-Watson:                   2.094
Prob(Omnibus):                  0.925   Jarque-Bera (JB):                0.266
Skew:                          -0.183   Prob(JB):                        0.876
Kurtosis:                       2.530   Cond. No.                         132.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



SomMot & medf_pc
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                medf_pc   R-squared:                       0.035
Model:                            OLS   Adj. R-squared:                 -0.093
Method:                 Least Squares   F-statistic:                    0.2757
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.763
Time:                        19:10:06   Log-Likelihood:                -63.285
No. Observations:                  18   AIC:                             132.6
Df Residuals:                      15   BIC:                             135.2
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
Intercept         95.1220     33.596      2.831      0.013      23.513     166.731
avgCEST_SomMot    -2.3619      3.945     -0.599      0.558     -10.772       6.048
SomMot             5.0659     18.604      0.272      0.789     -34.587      44.719
==============================================================================
Omnibus:                        4.345   Durbin-Watson:                   2.435
Prob(Omnibus):                  0.114   Jarque-Bera (JB):                1.407
Skew:                          -0.068   Prob(JB):                        0.495
Kurtosis:                       1.637   Cond. No.                         132.
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=18
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



Limbic & tap_tot
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                tap_tot   R-squared:                       0.482
Model:                            OLS   Adj. R-squared:                  0.352
Method:                 Least Squares   F-statistic:                     3.716
Date:                Tue, 16 Jan 2024   Prob (F-statistic):             0.0722
Time:                        19:10:06   Log-Likelihood:                -40.228
No. Observations:                  11   AIC:                             86.46
Df Residuals:                       8   BIC:                             87.65
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
Intercept         97.8589     14.960      6.542      0.000      63.362     132.356
avgCEST_Limbic    -4.3827      1.975     -2.220      0.057      -8.936       0.171
Limbic            79.2752     33.219      2.386      0.044       2.672     155.879
==============================================================================
Omnibus:                        1.519   Durbin-Watson:                   1.950
Prob(Omnibus):                  0.468   Jarque-Bera (JB):                0.766
Skew:                           0.629   Prob(JB):                        0.682
Kurtosis:                       2.702   Cond. No.                         73.9
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=11
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



Limbic & er40_cr
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                er40_cr   R-squared:                       0.544
Model:                            OLS   Adj. R-squared:                  0.430
Method:                 Least Squares   F-statistic:                     4.774
Date:                Tue, 16 Jan 2024   Prob (F-statistic):             0.0432
Time:                        19:10:06   Log-Likelihood:                -16.568
No. Observations:                  11   AIC:                             39.14
Df Residuals:                       8   BIC:                             40.33
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
Intercept         33.4960      1.789     18.719      0.000      29.370      37.622
avgCEST_Limbic    -0.1002      0.334     -0.300      0.772      -0.870       0.670
Limbic            10.3767      3.987      2.603      0.031       1.184      19.570
==============================================================================
Omnibus:                        1.586   Durbin-Watson:                   1.953
Prob(Omnibus):                  0.453   Jarque-Bera (JB):                1.081
Skew:                           0.699   Prob(JB):                        0.583
Kurtosis:                       2.366   Cond. No.                         69.8
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=11
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "



Limbic & medf_pc
                            OLS Regression Results                            
==============================================================================
Dep. Variable:                medf_pc   R-squared:                       0.313
Model:                            OLS   Adj. R-squared:                  0.142
Method:                 Least Squares   F-statistic:                     1.826
Date:                Tue, 16 Jan 2024   Prob (F-statistic):              0.222
Time:                        19:10:07   Log-Likelihood:                -36.007
No. Observations:                  11   AIC:                             78.01
Df Residuals:                       8   BIC:                             79.21
Df Model:                           2                                         
Covariance Type:            nonrobust                                         
==================================================================================
                     coef    std err          t      P>|t|      [0.025      0.975]
----------------------------------------------------------------------------------
Intercept         67.3799     10.192      6.611      0.000      43.878      90.882
avgCEST_Limbic     2.5443      1.345      1.891      0.095      -0.558       5.646
Limbic           -12.9089     22.632     -0.570      0.584     -65.098      39.280
==============================================================================
Omnibus:                        1.436   Durbin-Watson:                   2.083
Prob(Omnibus):                  0.488   Jarque-Bera (JB):                0.928
Skew:                          -0.414   Prob(JB):                        0.629
Kurtosis:                       1.842   Cond. No.                         73.9
==============================================================================

Notes:
[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.

/Users/pecsok/anaconda3/lib/python3.11/site-packages/scipy/stats/_stats_py.py:1806: UserWarning: kurtosistest only valid for n>=20 ... continuing anyway, n=11
  warnings.warn("kurtosistest only valid for n>=20 ... continuing "
In [145]:
#BBS2

from sklearn import linear_model
import statsmodels.api as sm
import statsmodels.formula.api as smf
import numpy as np
import matplotlib.pyplot as plt

if run_grpanalysis:
    # Curate data 
    value_counts = grp_df['hstatus'].value_counts()
    print(value_counts)
    grp_df['hstatus'] = grp_df['hstatus'].replace('NC', 'HC')
    grp_df['hstatus'] = grp_df['hstatus'].replace('PROR', 'PSY')
    grp_df['hstatus'] = grp_df['hstatus'].replace('PRO', 'PSY')
    grp_df['hstatus'] = grp_df['hstatus'].replace('S', 'PSY')
    grp_df['hstatus'] = grp_df['hstatus'].replace('O', 'Other')
    grp_df['hstatus'] = grp_df['hstatus'].replace('Unknown', 'Other')
    grp_df['hstatus'] = grp_df['hstatus'].replace('MDD', 'Other')
    value_counts = grp_df['hstatus'].value_counts()
    print(value_counts)
    
    colors = pd.DataFrame({'Network': ["Cont", "Default", "DorsAttn", "Vis", "SalVentAttn", "SomMot", "Limbic"],
        'Color': ['PuOr', 'PuRd_r', 'PiYG_r', 'PRGn', 'PiYG', 'GnBu_r', 'terrain_r']}) # 
    
    anova_tables = []
    # Create a scatter plot with a multiple linear regression 
    for network in networks:
        cestcol = "avgCEST_" + network
        # Create a linear regression model for fcon
       # color = colors.loc[colors['Network'] == network, 'Color'].values[0]
       # sns.set_palette(color)

        # Create CNB correlation plot for each network fcon and cest 
        for CNB_score in CNB_scores:
         #   graph_df = grp_df[grp_df['hstatus'] != 'Other']
            graph_df = grp_df
            graph_df = graph_df.dropna(subset=[CNB_score, cestcol, network])
            graph_df = graph_df[[CNB_score, cestcol, network]]
            # Define x values and target variable
            X = graph_df[[cestcol, network]]
            Y = graph_df[CNB_score]
            
            ##################################
            # Define formula and model
            #formula = f'{CNB_score} ~ {cestcol} + {network}'
            #model = smf.ols(formula=formula, data=graph_df).fit()
            #print(network + CNB_score)
            #print(model.summary())
            print(graph_df)
            fig = plt.figure()
            ax = fig.add_subplot(111, projection = '3d')
            ax.scatter(graph_df[CNB_score], graph_df[cestcol], graph_df[network])
            ax.set_xlabel(CNB_score)
            ax.set_ylabel(cestcol)
            ax.set_zlabel(network)
            plt.show()
            
            

hstatus
HC       10
Other    10
PSY       9
Name: count, dtype: int64
hstatus
HC       10
Other    10
PSY       9
Name: count, dtype: int64
      tap_tot  avgCEST_Cont      Cont
3   103.33330      8.684751  0.121458
7    92.00000      8.109159  0.096672
10  105.33330      8.515892  0.246400
13  116.33330      9.195672  0.222760
14   92.66666      7.579884  0.341071
15   93.33333      8.099021  0.165071
16  117.00000      7.687283  0.169243
17  103.33330      8.099098  0.261861
18  128.00000      8.151035  0.231845
19  120.00000      7.851769  0.201449
24   96.33333      8.402123  0.233773
25  110.00000      8.444942  0.201716
26  101.66670      8.653494  0.243740
27   92.00000      7.956718  0.119005
30  104.66670      8.590144  0.102177
31  125.66670      8.391536  0.128059

    er40_cr  avgCEST_Cont      Cont
0      39.0      8.496428  0.175604
3      38.0      8.684751  0.121458
4      39.0      8.097737  0.248519
7      34.0      8.109159  0.096672
10     39.0      8.515892  0.246400
13     37.0      9.195672  0.222760
15     37.0      8.099021  0.165071
16     37.0      7.687283  0.169243
17     36.0      8.099098  0.261861
18     38.0      8.151035  0.231845
19     38.0      7.851769  0.201449
24     40.0      8.402123  0.233773
25     38.0      8.444942  0.201716
26     38.0      8.653494  0.243740
27     40.0      7.956718  0.119005
30     39.0      8.590144  0.102177
31     36.0      8.391536  0.128059

      medf_pc  avgCEST_Cont      Cont
3   77.777778      8.684751  0.121458
7   69.444444      8.109159  0.096672
10  77.777778      8.515892  0.246400
13  69.444444      9.195672  0.222760
14  72.222222      7.579884  0.341071
15  80.555556      8.099021  0.165071
16  86.111111      7.687283  0.169243
17  86.111111      8.099098  0.261861
18  66.666667      8.151035  0.231845
19  91.666667      7.851769  0.201449
24  69.444444      8.402123  0.233773
25  88.888889      8.444942  0.201716
26  75.000000      8.653494  0.243740
27  80.555556      7.956718  0.119005
30  83.333333      8.590144  0.102177
31  86.111111      8.391536  0.128059

      tap_tot  avgCEST_Default   Default
2   104.00000         7.469096  0.260692
3   103.33330         8.128972  0.322248
7    92.00000         7.698660  0.101490
10  105.33330         7.951705  0.168437
11   97.00000         7.705413  0.235045
13  116.33330         8.244459  0.233656
14   92.66666         8.285107  0.069097
15   93.33333         7.263857  0.196767
16  117.00000         7.310467  0.402548
17  103.33330         7.649832  0.323492
18  128.00000         7.537559  0.195510
19  120.00000         7.571108  0.240199
25  110.00000         7.758677  0.156970
26  101.66670         8.420564  0.267923
27   92.00000         8.445907  0.399663
30  104.66670         7.303762  0.097644
31  125.66670         7.966794  0.364319

    er40_cr  avgCEST_Default   Default
0      39.0         7.605122  0.376170
2      38.0         7.469096  0.260692
3      38.0         8.128972  0.322248
4      39.0         7.356575  0.331945
7      34.0         7.698660  0.101490
10     39.0         7.951705  0.168437
11     39.0         7.705413  0.235045
13     37.0         8.244459  0.233656
15     37.0         7.263857  0.196767
16     37.0         7.310467  0.402548
17     36.0         7.649832  0.323492
18     38.0         7.537559  0.195510
19     38.0         7.571108  0.240199
25     38.0         7.758677  0.156970
26     38.0         8.420564  0.267923
27     40.0         8.445907  0.399663
30     39.0         7.303762  0.097644
31     36.0         7.966794  0.364319

      medf_pc  avgCEST_Default   Default
2   88.888889         7.469096  0.260692
3   77.777778         8.128972  0.322248
7   69.444444         7.698660  0.101490
10  77.777778         7.951705  0.168437
11  66.666667         7.705413  0.235045
13  69.444444         8.244459  0.233656
14  72.222222         8.285107  0.069097
15  80.555556         7.263857  0.196767
16  86.111111         7.310467  0.402548
17  86.111111         7.649832  0.323492
18  66.666667         7.537559  0.195510
19  91.666667         7.571108  0.240199
25  88.888889         7.758677  0.156970
26  75.000000         8.420564  0.267923
27  80.555556         8.445907  0.399663
30  83.333333         7.303762  0.097644
31  86.111111         7.966794  0.364319

      tap_tot  avgCEST_DorsAttn  DorsAttn
2   104.00000          6.382527  0.258377
3   103.33330          6.035932  0.287490
5    72.33333          6.972563  0.316759
7    92.00000          8.559614  0.063928
10  105.33330          8.223989  0.243422
11   97.00000          8.287902  0.429669
13  116.33330         10.419630  0.278428
14   92.66666          7.080233  0.176538
15   93.33333          7.078968  0.330240
16  117.00000          6.118033  0.309288
17  103.33330          7.992198  0.510831
18  128.00000          8.277535  0.200455
19  120.00000          8.579904  0.335207
24   96.33333          8.066102  0.401307
25  110.00000          7.902069  0.219597
26  101.66670          7.363532  0.494674
27   92.00000          8.043687  0.631075
30  104.66670          8.293079  0.313409
31  125.66670          8.892468  0.473277

    er40_cr  avgCEST_DorsAttn  DorsAttn
0      39.0          7.654973  0.402285
2      38.0          6.382527  0.258377
3      38.0          6.035932  0.287490
4      39.0          9.062175  0.520766
7      34.0          8.559614  0.063928
10     39.0          8.223989  0.243422
11     39.0          8.287902  0.429669
13     37.0         10.419630  0.278428
15     37.0          7.078968  0.330240
16     37.0          6.118033  0.309288
17     36.0          7.992198  0.510831
18     38.0          8.277535  0.200455
19     38.0          8.579904  0.335207
24     40.0          8.066102  0.401307
25     38.0          7.902069  0.219597
26     38.0          7.363532  0.494674
27     40.0          8.043687  0.631075
30     39.0          8.293079  0.313409
31     36.0          8.892468  0.473277

      medf_pc  avgCEST_DorsAttn  DorsAttn
2   88.888889          6.382527  0.258377
3   77.777778          6.035932  0.287490
5   91.666667          6.972563  0.316759
7   69.444444          8.559614  0.063928
10  77.777778          8.223989  0.243422
11  66.666667          8.287902  0.429669
13  69.444444         10.419630  0.278428
14  72.222222          7.080233  0.176538
15  80.555556          7.078968  0.330240
16  86.111111          6.118033  0.309288
17  86.111111          7.992198  0.510831
18  66.666667          8.277535  0.200455
19  91.666667          8.579904  0.335207
24  69.444444          8.066102  0.401307
25  88.888889          7.902069  0.219597
26  75.000000          7.363532  0.494674
27  80.555556          8.043687  0.631075
30  83.333333          8.293079  0.313409
31  86.111111          8.892468  0.473277

      tap_tot  avgCEST_Vis       Vis
2   104.00000     8.967306  0.396056
3   103.33330     9.610587  0.468854
5    72.33333    12.741945  0.274933
10  105.33330     9.746861  0.738115
11   97.00000    10.589238  0.509295
13  116.33330    12.802977  0.492354
14   92.66666    13.682704  0.967040
15   93.33333     9.176037  0.793801
16  117.00000     9.737379  0.309563
17  103.33330    10.190264  0.354786
18  128.00000     9.714172  0.335023
19  120.00000    10.088696  0.553575
24   96.33333    10.633214  0.342325
25  110.00000    12.498565  0.352182
26  101.66670     7.216132  0.351959
27   92.00000    12.373299  0.484798
30  104.66670    13.772174  0.297944
31  125.66670    12.511567  0.295938

    er40_cr  avgCEST_Vis       Vis
0      39.0    14.297329  0.594154
2      38.0     8.967306  0.396056
3      38.0     9.610587  0.468854
4      39.0     8.387602  0.525277
10     39.0     9.746861  0.738115
11     39.0    10.589238  0.509295
13     37.0    12.802977  0.492354
15     37.0     9.176037  0.793801
16     37.0     9.737379  0.309563
17     36.0    10.190264  0.354786
18     38.0     9.714172  0.335023
19     38.0    10.088696  0.553575
24     40.0    10.633214  0.342325
25     38.0    12.498565  0.352182
26     38.0     7.216132  0.351959
27     40.0    12.373299  0.484798
30     39.0    13.772174  0.297944
31     36.0    12.511567  0.295938

      medf_pc  avgCEST_Vis       Vis
2   88.888889     8.967306  0.396056
3   77.777778     9.610587  0.468854
5   91.666667    12.741945  0.274933
10  77.777778     9.746861  0.738115
11  66.666667    10.589238  0.509295
13  69.444444    12.802977  0.492354
14  72.222222    13.682704  0.967040
15  80.555556     9.176037  0.793801
16  86.111111     9.737379  0.309563
17  86.111111    10.190264  0.354786
18  66.666667     9.714172  0.335023
19  91.666667    10.088696  0.553575
24  69.444444    10.633214  0.342325
25  88.888889    12.498565  0.352182
26  75.000000     7.216132  0.351959
27  80.555556    12.373299  0.484798
30  83.333333    13.772174  0.297944
31  86.111111    12.511567  0.295938

      tap_tot  avgCEST_SalVentAttn  SalVentAttn
2   104.00000             7.277624     0.420864
3   103.33330             7.863291     0.243196
5    72.33333             7.181154     0.302949
7    92.00000             8.124917     0.186044
10  105.33330             7.937581     0.340396
11   97.00000             7.662420     0.424431
13  116.33330             8.939010     0.295722
14   92.66666             7.325671     0.505170
15   93.33333             7.404367     0.378813
16  117.00000             7.365407     0.467500
17  103.33330             7.774126     0.482222
18  128.00000             8.103207     0.437835
19  120.00000             7.989862     0.349883
24   96.33333             7.920916     0.533080
25  110.00000             8.152667     0.318753
26  101.66670             7.594341     0.273180
27   92.00000             8.100647     0.392901
30  104.66670             8.358057     0.159922
31  125.66670             8.547472     0.396681

    er40_cr  avgCEST_SalVentAttn  SalVentAttn
0      39.0             8.034846     0.371115
2      38.0             7.277624     0.420864
3      38.0             7.863291     0.243196
4      39.0             8.805663     0.222687
7      34.0             8.124917     0.186044
10     39.0             7.937581     0.340396
11     39.0             7.662420     0.424431
13     37.0             8.939010     0.295722
15     37.0             7.404367     0.378813
16     37.0             7.365407     0.467500
17     36.0             7.774126     0.482222
18     38.0             8.103207     0.437835
19     38.0             7.989862     0.349883
24     40.0             7.920916     0.533080
25     38.0             8.152667     0.318753
26     38.0             7.594341     0.273180
27     40.0             8.100647     0.392901
30     39.0             8.358057     0.159922
31     36.0             8.547472     0.396681

      medf_pc  avgCEST_SalVentAttn  SalVentAttn
2   88.888889             7.277624     0.420864
3   77.777778             7.863291     0.243196
5   91.666667             7.181154     0.302949
7   69.444444             8.124917     0.186044
10  77.777778             7.937581     0.340396
11  66.666667             7.662420     0.424431
13  69.444444             8.939010     0.295722
14  72.222222             7.325671     0.505170
15  80.555556             7.404367     0.378813
16  86.111111             7.365407     0.467500
17  86.111111             7.774126     0.482222
18  66.666667             8.103207     0.437835
19  91.666667             7.989862     0.349883
24  69.444444             7.920916     0.533080
25  88.888889             8.152667     0.318753
26  75.000000             7.594341     0.273180
27  80.555556             8.100647     0.392901
30  83.333333             8.358057     0.159922
31  86.111111             8.547472     0.396681

      tap_tot  avgCEST_SomMot    SomMot
3   103.33330        8.048849  0.476986
5    72.33333        6.923363  0.530501
7    92.00000        8.360912  0.207787
10  105.33330        7.416647  0.458101
11   97.00000        7.336883  0.495047
13  116.33330        8.874376  0.438165
14   92.66666        7.077155  0.330849
15   93.33333        7.627305  0.276265
16  117.00000        6.892677  0.389875
17  103.33330        8.036966  0.519093
18  128.00000        8.222816  0.371118
19  120.00000        7.866533  0.616235
24   96.33333        7.681239  0.631874
25  110.00000        7.964384  0.323271
26  101.66670        7.400841  0.457556
27   92.00000        7.430732  0.372234
30  104.66670        8.525817  0.221894
31  125.66670        8.374417  0.397562

    er40_cr  avgCEST_SomMot    SomMot
0      39.0        7.723693  0.504323
3      38.0        8.048849  0.476986
4      39.0        8.941838  0.517550
7      34.0        8.360912  0.207787
10     39.0        7.416647  0.458101
11     39.0        7.336883  0.495047
13     37.0        8.874376  0.438165
15     37.0        7.627305  0.276265
16     37.0        6.892677  0.389875
17     36.0        8.036966  0.519093
18     38.0        8.222816  0.371118
19     38.0        7.866533  0.616235
24     40.0        7.681239  0.631874
25     38.0        7.964384  0.323271
26     38.0        7.400841  0.457556
27     40.0        7.430732  0.372234
30     39.0        8.525817  0.221894
31     36.0        8.374417  0.397562

      medf_pc  avgCEST_SomMot    SomMot
3   77.777778        8.048849  0.476986
5   91.666667        6.923363  0.530501
7   69.444444        8.360912  0.207787
10  77.777778        7.416647  0.458101
11  66.666667        7.336883  0.495047
13  69.444444        8.874376  0.438165
14  72.222222        7.077155  0.330849
15  80.555556        7.627305  0.276265
16  86.111111        6.892677  0.389875
17  86.111111        8.036966  0.519093
18  66.666667        8.222816  0.371118
19  91.666667        7.866533  0.616235
24  69.444444        7.681239  0.631874
25  88.888889        7.964384  0.323271
26  75.000000        7.400841  0.457556
27  80.555556        7.430732  0.372234
30  83.333333        8.525817  0.221894
31  86.111111        8.374417  0.397562

      tap_tot  avgCEST_Limbic    Limbic
2   104.00000       10.076208  0.556081
3   103.33330        5.855585  0.557474
5    72.33333       10.419856  0.355055
7    92.00000        5.411261  0.219353
10  105.33330        7.552845  0.553482
13  116.33330        6.410949  0.470029
15   93.33333        4.421310  0.308839
17  103.33330        6.229536  0.343770
24   96.33333        7.509117  0.488638
26  101.66670        4.831669  0.323012
31  125.66670        6.177131  0.434284

    er40_cr  avgCEST_Limbic    Limbic
2      38.0       10.076208  0.556081
3      38.0        5.855585  0.557474
4      39.0        6.432389  0.625961
7      34.0        5.411261  0.219353
10     39.0        7.552845  0.553482
13     37.0        6.410949  0.470029
15     37.0        4.421310  0.308839
17     36.0        6.229536  0.343770
24     40.0        7.509117  0.488638
26     38.0        4.831669  0.323012
31     36.0        6.177131  0.434284

      medf_pc  avgCEST_Limbic    Limbic
2   88.888889       10.076208  0.556081
3   77.777778        5.855585  0.557474
5   91.666667       10.419856  0.355055
7   69.444444        5.411261  0.219353
10  77.777778        7.552845  0.553482
13  69.444444        6.410949  0.470029
15  80.555556        4.421310  0.308839
17  86.111111        6.229536  0.343770
24  69.444444        7.509117  0.488638
26  75.000000        4.831669  0.323012
31  86.111111        6.177131  0.434284
In [ ]:
!jupyter nbconvert --to html motor_pipeline.ipynb --output motor_pipeline_3T.html